Artificial intelligence in cancer diagnosis and therapy using sex and gender as precision biomarkers

Abstract

Sex and gender represent critical yet underutilized precision biomarkers in oncology, influencing cancer incidence, progression, treatment response, and survival outcomes. Biological sex, characterized by chromosomal, hormonal, and anatomical factors, directly influences tumor biology, immune responses, and therapy-related toxicities. Gender, encompassing social norms and behaviors, further impacts cancer risk through lifestyle choices, health awareness, and access to healthcare. Nonetheless, the predominant portion of clinical research remains gender- and sex-neutral, hence limiting the efficacy of precision medicine. Artificial intelligence (AI) facilitates predictive modeling for diagnosis, therapy optimization, and prognosis by integrating sex- and gender-specific data, ranging from high-dimensional biological markers to actual clinical records. In conditions such as breast, prostate, and lung cancers, AI-driven patient stratification can discern sex-specific biological pathways, guide the formulation of targeted treatments, and improve therapy customization. Despite these advancements, challenges about data representation, annotation, and regulatory compliance persist. To attain equitable precision oncology, ethically governed AI frameworks must be integrated with extensive, longitudinal, and gender-balanced datasets. Consistently incorporating sex and gender across the cancer care continuum for diverse patient populations may enhance quality of life, augment therapeutic efficacy, and reduce disparities.

Introduction

The global burden of cancer continues to rise, drawing increasing attention to the role of sex and gender in understanding and managing this complex disease [24]. Although the terms sex and gender are often used as if they are interchangeable, they represent different concepts that have important implications in cancer research and treatment. Sex refers to biological factors such as chromosomes, hormone levels, and reproductive anatomy, whereas gender relates to social roles, behaviors, expectations, and identities linked to being male or female [37]. This distinction is critical, as both dimensions influence cancer care and outcomes in unique ways.

Research consistently shows differences in cancer incidence, prevalence, and mortality between men and women [66]. For example, breast cancer occurs at a much higher rate in women, while prostate cancer is exclusive to men. Beyond these obvious differences, there are cancers where outcomes and treatment responses also vary by sex. Men and women can respond differently to the same therapies, reflecting the combined impact of physiological, genetic, and behavioral factors [65]. Hormones, in particular, play an important role. Estrogen, for instance, has been linked to the development and progression of breast cancer, and treatments targeting hormones are increasingly tailored according to biological sex [63]. Genetics also contributes to these disparities, as variations in mutations and responses to treatment differ between men and women, reinforcing the importance of personalized medicine that accounts for sex [9].

Behavioral influences, often shaped by gender norms, also affect cancer risk and outcomes. Lifestyle choices such as smoking, diet, and exercise frequently differ between men and women due to social expectations, which can contribute to varying cancer rates and prognoses [22]. This demonstrates the need to address both biological and social aspects when developing prevention strategies. Precision medicine, which aims to tailor treatment to the unique characteristics of each patient, benefits greatly from incorporating sex and gender. Such an approach not only improves treatment effectiveness but also helps reduce side effects [37]. To make this possible, healthcare providers must remain aware of how sex and gender identity shape individual experiences with cancer [24].

Finally, there is a strong need for more inclusive research that considers sex and gender as fundamental variables. Many clinical trials in the past have failed to account for these factors, creating knowledge gaps and potential inequalities in care [22]. Expanding representation in cancer studies will provide a clearer understanding of how biological and social differences affect the disease, which in turn can lead to more equitable and effective healthcare practices. This review was conducted following a systematic approach to ensure comprehensive coverage of the literature. We searched major databases, including PubMed, Scopus, Embase, Web of Science, and Google Scholar. Keywords included combinations such as “sex differences AND cancer”, “gender disparities AND oncology”, “sex biomarkers AND precision medicine”, “gender AND AI cancer diagnosis”, and “sex-specific AND cancer therapy” with Boolean operators (AND/OR). The search covered publications from 2010 to February 2026 to capture recent AI advancements in oncology. Inclusion criteria focused on peer-reviewed articles, reviews, and clinical studies addressing sex/gender in cancer diagnosis, AI applications, biomarkers, or therapy; English-language only. Exclusion criteria omitted non-cancer studies, animal-only research without human relevance, and non-peer-reviewed sources [24, 26, 37].

Titles and abstracts from ~ 1,500 initial hits were screened by two authors (AK and NN), yielding 320 full-text reviews. Ultimately, 120 studies were included based on relevance to sex/gender as precision biomarkers with AI integration. No formal quality appraisal (e.g., AMSTAR-2) was applied, as this is a narrative review emphasizing conceptual synthesis over meta-analysis; however, priority was given to high-impact journals (e.g., Lancet, Nature Reviews). We excluded articles that were not focused on oncology, non–peer-reviewed opinion pieces lacking primary data or substantive conceptual frameworks, conference abstracts without a corresponding full-text publication, non-English articles, and studies that neither reported sex- nor gender-stratified information nor addressed biomarkers, precision oncology, or AI/ML applications [73]. Additional references were identified through manual screening of the reference lists of key primary studies, systematic reviews, and relevant policy or guidance documents. Because the objective was to provide a broad, interdisciplinary synthesis rather than a PRISMA-compliant systematic review or meta-analysis, we did not perform formal risk-of-bias assessments; instead, we focused on mapping the breadth of available evidence, recurring methodological patterns, and major gaps that have implications for sex- and gender-aware AI in oncology [16, 69].

The growing global burden of cancer requires a careful re-evaluation of how sex and gender are conceptualized in oncology. Explicitly recognising the distinct biological and social contributions to cancer incidence, treatment response, and outcomes enables the design of more targeted and patient-centred interventions [65]. Such an approach can improve therapeutic effectiveness, reduce adverse events, and ultimately enhance quality of care for people with cancer. Consequently, future research and clinical practice should routinely integrate sex- and gender-related variables as fundamental components of study design, prevention strategies, and treatment planning. Figure 1 illustrates AI-driven strategies that integrate sex and gender as precision biomarkers to enhance cancer diagnosis, treatment selection, and outcome prediction. By leveraging machine learning and biomarker profiling, these approaches enable more personalized and equitable cancer care.

Fig. 1.

AI-driven approaches to integrate sex and gender as precision biomarkers in cancer diagnosis, therapy, and outcome prediction

Table 1 and Fig. 2 outline essential biological (sex) and behavioral/social (gender) factors influencing cancer risk, progression, and treatment response. It distinguishes inherent biological characteristics from adjustable social and cultural behaviors, highlighting their interplay in cancer outcomes. Grasping these factors is crucial for tailored medical approaches that tackle inequalities in diagnosis, treatment, and outcomes.

Table 1.

Biological and behavioral factors influencing cancer by sex and gender

Factor type	Variable	Impact on cancer	Example mechanism	References
Biological	Chromosomal differences (XX vs XY)	Alters gene expression	BRCA1 mutations in breast cancer risk	[9]
Biological	Hormonal milieu	Modifies tumor growth rates	Estrogen in ER + breast cancer	[63]
Biological	Immune system variation	Influences immunotherapy response	Higher CD4 + activity in females	[28]
Biological	Body composition	Affects drug metabolism	Higher fat percentage alters pharmacokinetics	[37]
Biological	Microbiome differences	Modulates carcinogenesis	Gut microbiota and colorectal cancer	[28]
Behavioral	Smoking rates	Alters cancer incidence	Higher male prevalence in lung cancer	[22]
Behavioral	Diet patterns	Affects metabolic cancer risk	Processed meat and colorectal cancer	[24]
Behavioral	Physical activity	Reduces certain cancer risks	Exercise reduces post-menopausal cancer	[65]
Behavioral	Health-seeking behavior	Delays diagnosis	Gender norms reducing screening uptake	[59]
Behavioral	Alcohol use	Impacts multiple cancer types	Alcohol and liver cancer prevalence	[22]

Fig. 2.

Biological and behavioral determinants of cancer risk, stratified by sex and gender, highlighting their differential impact on disease susceptibility

Novel contribution of this review

Unlike prior reviews that have considered sex and gender differences in oncology or artificial intelligence applications in cancer care in relative isolation, this article explicitly examines their convergence across the entire cancer care continuum. It synthesizes evidence on how biological sex and socially constructed gender roles shape cancer risk, tumor biology, treatment response, toxicity, and quality-of-life outcomes, and maps these dimensions onto current and emerging AI- and machine-learning–based tools for diagnosis, prognostication, therapy selection, monitoring, and survivorship care. By integrating molecular, clinical, behavioral, ethical, and policy perspectives, the review proposes a conceptual framework for embedding sex- and gender-aware biomarkers into AI pipelines in oncology, identifies methodological and data-related barriers, and highlights priority avenues for future research and implementation.

Understanding sex and gender in oncology

Definitions of sex and gender have been offered by the U.S. Government Accountability Office (GAO) and other agencies. The “sex” of a person is defined as male or female according to biology and genetics, including sex chromosomes, gene expression, hormone levels and function, and reproductive/sexual anatomy. Variations may occur due to variations in sex chromosomes and genes, as well as endogenous (biological) and exogenous (environmental) hormone levels and function. Sex is classified as a binary variable but can have diverse manifestations [36]. The term “intersex” refers to a particular set of rare conditions related to diversity in sex development or characteristics. The American Psychiatric Association (APA) defines intersex people as those who have “a variety of differences in development of reproductive anatomy and chromosomal patterns” (“Intersex” may also be used as an adjective for such differences). Individuals with mixed gonadal dysgenesis may be considered intersex. A disorder of sex development (DSD) is a congenital “condition in which development of chromosomal, gonadal, or anatomical sex is atypical.” Some intersex people, such as those with congenital adrenal hyperplasia or androgen insensitivity syndrome, do not have DSDs. The term “sex at birth” refers to the sex classification stated on one’s original birth certificate or similar official identification documents.

In many cases, the sex at birth is the sex assigned at birth, but it may also be based on a biological indicator around birth or be a retroactive classification, based on molecular or genetic testing. “Gender” is conceptually distinct; it encompasses socially constructed roles, behaviours, expressions, and identities of girls, women, boys, men, and gender-diverse people [49]. Gender influences how people perceive themselves and each other, how they act and interact, and the distribution of power and resources in society. Gender influences self-perception and social interactions on many levels, and transhistorical and transcultural variations have been documented. When used to describe a person (or described as an attribute of a person), the term “gender” refers to the person’s gender identity (a person’s internal sense of their gender). Several systems exist to categorise gender (such as the World Health Organization’s International Classification of Diseases), but they are not universally used. “Sex” and “gender” are separate,while they are related and often used interchangeably in common usage, they are not synonymous, nor are they opposites or two parts of a whole [36].

Definitions and distinctions

Sex and gender are key factors in health and disease. Sex describes biological components based on chromosomal differences, including cellular and molecular distinctions. Typically, females have XX chromosomes and males have XY chromosomes; genes on these chromosomes play crucial roles in reproductive development and hormone production [15]. Sex influences disease prevalence, diagnosis, prognosis, and therapeutic responses, with females exhibiting physiological characteristics that affect disease development and treatment toxicity, including greater susceptibility to chemotherapy toxicity [51]. Gender encompasses social roles and behaviors such as income, nutrition, environmental exposures, and lifestyle, which affect health outcomes. Societal expectations and cultural norms contribute to gender-based differences in health and access to health services and information. Because they affect cancer epidemiology, prognosis, and treatment, “sex” and “gender” deserve to be treated as precision biomarkers in cancer diagnosis, therapy, and survival prediction. However, clinical decision-making frequently neglects sex [74]. A majority of biomarker studies aim for sex- and/or gender-neutrality, and meta-analyses of adverse drug reactions (ADRs) do not consistently report sex-specific outcomes [45, 53, 74]. The question of how to exploit sex and gender as biomarkers for cancer remains open in research and practice.

Throughout this review, we use the term sex to denote biological attributes such as chromosomal complement, gonadal and reproductive anatomy, hormone levels, and associated molecular and physiological traits. We use the term gender to refer to socially constructed roles, norms, behaviors, expressions, and identities, including gendered patterns of exposure, health‑seeking behavior, and access to care [36, 49]. Where we explicitly discuss the inseparable interaction of these dimensions, or where data sources do not allow a clean separation, we may use the combined term sex/gender and state this explicitly in the text. In the case studies (Sect. 7), labels such as “male”, “female”, “men”, and “women” refer to biological sex unless otherwise specified. In contrast, references to social roles, norms, or behaviors (for example, smoking patterns, occupational exposures, or screening uptake) are described as gendered influences [37].

Historical perspectives

Cancer is the second-leading cause of death worldwide and a major contributor to the disease burden. Disparities in incidence, malignancy, and mortality rates between male and female patients with cancer have been identified for most cancers [66]. Sex differences are observed worldwide and exist in most tumor types. Sex is a vital biological factor that affects cancer development and progression; the authors of several papers and health agencies have recommended considering sex differences in translational, basic, and clinical cancer research [24]. Gaining a deeper understanding of the influence of sex- and gender-based differences in the development and progression of cancer and the subsequent specific treatments, which also take into consideration the adverse drug reactions, advances our ability to improve the efficacy of cancer treatment via the development of targeted treatments [51]. The clinical data collected in the daily practice of medicine can provide an essential resource that is uniquely specific to the individual patient. A significant amount of information related to tumor evolution and the clinical state of the patient is contained in the data, which, when combined with scientific knowledge, can lead to increasingly age- and sex/gender-specific diagnosis and therapy.

Operationalization in clinical datasets for AI

Although gender and sex are conceptually different, they are frequently not adequately addressed in clinical datasets, despite the fact that doing so is essential for reliable AI modeling in oncology. One of the main obstacles is separating current gender identity (self-reported, possibly nonbinary) from sex assigned at birth (usually binary, from birth records or genetics), which many electronic health records (EHRs) confuse or omit [74]. Hormonal status makes capture even more difficult. Because they alter tumor biology and treatment responses (e.g., estrogen levels in breast cancer), menopausal status, endocrine therapies, gender-affirming hormone therapy (GAHT), or hormone replacement therapy (HRT) must be extracted as time-varying covariates [59]. With a prevalence of approximately 1.7%, intersex variations and disorders/differences of sex development (DSD) require inclusive coding beyond binary categories, requiring explicit handling to prevent misclassification [15].

Nonbinary gender categories, which are becoming more widely accepted in systems such as WHO’s ICD-11, require flexible encoding (e.g., “other/unknown/prefer not to say”). Additionally, AI models may be biased by missingness (up to 10–20% in EHRs) and misclassification; imputation techniques or sensitivity analyses are crucial [30, 49]. Lastly, as discussed in AI integration talks, natural language processing (NLP) tools allow for extraction from unstructured clinical notes (e.g., identifying “transgender female on HRT”), but they need to be validated for accuracy. FHIR ontologies should be used to standardize these in future datasets for fair precision oncology [18].

AI and machine learning in cancer research

Artificial intelligence (AI) and its subfield, machine learning (ML), can transform the scale and speed at which progress in cancer research is attained [16]. AI has demonstrated the ability to automatically emulate clinical decision-making, specializing in specialized annotation tasks and nuanced diagnoses. For example, AI can be trained on annotated data to recognize particular patterns of tumors and cancer cells for tasks such as survival rate prediction, classification, segmentation, prognosis prediction, or treatment planning [23]. The sheer variety of AI models that have been proposed has resulted in high-dimensional clinical, pathological, and genomic datasets being mined for both prognostic and predictive cancer biomarkers [32]. Given the intricacies of particular cancers and patient-level covariates, the increasing complexity of deep learning models is well-suited to determining the manifestations of sex and gender in cancer. Figure 3 highlights how artificial intelligence transforms cancer diagnosis, treatment planning, and outcome prediction. By leveraging machine learning, clinicians can uncover hidden patterns in complex datasets, enabling highly personalized and precise cancer care.

Fig. 3.

The AI revolution in oncology: harnessing machine learning for precision cancer care

Overview of AI technologies

Major information technology advances over the past two decades have included a rapid expansion of the use of artificial intelligence (AI) techniques to support data-driven decisions and discoveries in biology and medicine [60]. AI methods have become increasingly useful in medical research and practice where the data involved tend to be voluminous, and their highly nonlinear interrelationships often too intricate to be untangled by conventional statistical methods [69]. Within cancer research and care, AI supplements the processing of important information and the navigation of large databases to enable prompt and precise decisions and actions. Potential AI applications include diagnosis, discovery, and development of new treatments,selection of treatment options; treatment monitoring and verification; prognosis prediction; and management of adjuvant problems such as pain, medication doses, and psychological outcomes [8].

Table 2 outlines applications of artificial intelligence in integrating sex and gender variables into cancer care. The focus is on diagnosis, therapy optimization, and survival prediction, using multimodal datasets. AI can uncover hidden sex-based patterns in tumor profiles, enhancing personalized oncology.

Table 2.

AI applications in sex- and gender-specific cancer research

AI application	Cancer type	Data type used	Sex/gender role	Clinical benefit
Risk prediction models	Breast	Genomics + Imaging	Female-specific risk stratification	Early detection
Radiomics	Lung	CT images	Sex-stratified tumor texture analysis	Improved classification
Drug response modeling	Prostate	Genomics + EHR	Male-specific hormonal pathway analysis	Therapy matching
Prognostic modeling	Colorectal	Histopathology	Sex-segregated survival curves	Survival prediction
Toxicity prediction	Multiple	Clinical trial data	Female predisposition to ADR	Dose optimization
Screening AI	Cervical	Cytology	Female-only disease detection	Automated reporting
Biomarker discovery	Melanoma	Genomic + Proteomic	Sex-stratified biomarker panels	Targeted therapy
Temporal analytics	Breast	Longitudinal EHR	Hormonal cycle-linked patterns	Timing treatments
Multimodal fusion	Lung	Genomics + Imaging + Lifestyle	Integrates gender behaviors	Holistic prognosis
Public health surveillance	Multiple	Population registries	Gender-disaggregated incidence	Policy making

Applications in oncology

Sex and gender have attracted considerable interest as precision cancer biomarkers because of their multifaceted effects on the genomics, epigenetics, transcriptomics, proteomics, metabolomics, and immunological characteristics of tumours, their impacts on cancer incidence, histopathological phenotype, aggressiveness, and outcome, and their relationships to environmental and lifestyle exposures [24]. Applications of artificial intelligence (AI) and machine learning are facilitating the collection of data on these intricate relationships, and models that incorporate sex and gender, together with tumour and treatment information, are enabling better prediction of diagnosis, prognosis, response to treatment, adverse events, and quality of life [72]. Cancer is a broad range of disorders that are unified by the characteristic of uncontrolled, or dysregulated, proliferation of abnormal cells. Such dysregulated proliferation results first in the formation of a localised tumour and later in the spread of malignant cells throughout the body, a process known as metastasis. Tumours that have undergone malignant progression are responsible for over 90% of cancer deaths [21]. Estimates of cancer incidence and mortality indicate approximately 19.3 million new cases and 9.96 million deaths worldwide in 2020, with lung, breast, colorectal, prostate, and stomach cancer the most common by incidence and lung, colorectal, liver, stomach, and oesophageal cancer the most common by mortality [58]. Advanced AI technologies demonstrating remarkable capability for deriving insights from complex databases can be applied to sex- and gender-related differences in cancer.

Biomarkers in cancer: an overview

The Harvard Center for Cancer Systems Biology (Harvard CSBC) and the National Cancer Institute define a “biomarker” as a measured characteristic used as an indicator of normal biological or pathogenic processes, or biological responses to an exposure or intervention, including therapeutic interventions [57]. Examples include gene expression, protein concentration, and tumor size. Biomarkers aid in the identification and stratification of risk, diagnosis, and monitoring of cancer through minimally invasive methods, allowing cancer to be staged and graded.

Biomarkers can be classified into three types: prognostic, diagnostic, and predictive. Prognostic biomarkers, such as carbonic anhydrase IX (CAIX) for renal cell carcinoma, provide information about disease aggressiveness or recurrence risks. Diagnostic biomarkers, like prostate-specific antigen (PSA) for prostate cancer, are used to detect the presence. Predictive biomarkers assess the likelihood of a patient responding to a particular therapy, exemplified by the BRAF gene mutation in melanoma [67]. Personalized medicine, also known as precision medicine, stratifies patients into subpopulations differentially susceptible to a particular disease or treatment, ranging from broad characteristics like sex and gender to molecular and genetic markers [24]. In cancer diagnosis, prognosis, and therapy, sex and gender are critical determinants. Sex differences influence immune-related adverse events associated with immune checkpoint inhibitors, and sex-specific cytokine pathways and biomarkers play a crucial role in personalized strategies for cancer immunotherapy. Medication regimens that account for sex and gender differences can maximize efficacy while minimizing adverse reactions and toxicity [26].

Types of biomarkers

Biomarkers fall into multiple categories, including genomic, proteomic, histologic, physiologic, and radiographic [55]. A plethora of genomics-based biomarkers aid in diagnosing, staging, and monitoring cancer, as well as guiding therapeutic decisions. The search for new and improved biomarkers remains a priority in oncology, focused on early diagnosis and more accurate prognostication [40].

Four biomarker classes dominate clinical practice: diagnostic, prognostic, predictive, and pharmacodynamic. Diagnostic biomarkers ascertain the nature of abnormal tissue, prognostic biomarkers estimate overall tumor aggressiveness and outcome, predictive biomarkers forecast therapeutic success, and pharmacodynamic biomarkers delineate target engagement and downstream pathway modification [67]. Sex and gender constitute complementary biomarkers, serving as a discriminating variable that must be integrated into every type of biomarker. In this review, the term “biomarker” is used in an intentionally broad, precision-medicine-oriented sense. Specifically, sex and gender are treated primarily as higher-level stratification variables and effect modifiers that systematically shape the distribution, performance, and clinical interpretation of molecular, imaging, and clinical biomarkers, rather than as molecular analytes themselves [57]. Within this framework, references to sex and gender as “precision biomarkers” denote stable, routinely available patient characteristics that can be used to stratify risk, refine the predictive value of other biomarkers, and guide treatment selection and dosing [67]. This conceptualisation is consistent with contemporary precision oncology practice, in which demographic and clinical covariates such as age, comorbidities, and performance status are integrated with omics-based biomarkers to generate individualised prognostic and predictive models [24].

Role of biomarkers in precision medicine

The hallmarks of cancer, particularly metabolic reprogramming and immune evasion, exhibit significant sex disparities [24]. Molecular and genomic alterations along the hallmarks of cancer may thus be considered sex-specific or sex-biased biomarkers for cancer diagnosis, prognosis, and prediction of treatment response and toxicities. As the term “biomarker” lacks a unique and universally accepted definition, the WHO definition is adopted here. A biomarker is “any substance, structure, or process that can be measured in the body or its products and influence or predict the incidence of outcome or disease” [1]. The sex-specific or sex-biased molecular and genomics alterations along the hallmarks of cancer, measurable in body products, such as blood or biopsies, that influence or predict the incidence of cancer or its outcome, are to be considered as biomarkers. Within this framework, sex-specific biomarkers, which are measurable only in one of the two sexes, and sex-biased biomarkers, which are present in both sexes but show significant quantitative or qualitative differences, are distinguished (Fig. 4).

Fig. 4.

Multi-modal biomarkers driving personalized medicine: from genomic insights and ctDNA to imaging and proteomic profiling

The role of sex and gender as biomarkers

Biological sex and social gender have major impacts on health and disease, shaping differences in the bidirectional relations between cancer screening and diagnosis, and each of actual cancer rate, treatment, and recovery [26, 37]. Reproductive organs and the immune system are distinctly sex-dimensioned anatomically and molecularly [28, 37]. Despite these findings, research has conventionally centred on males [11]. Public agencies have issued guidelines to account for sex and gender as modulators of biology and medicine [42]. Still, most patient records lack the relevant annotations, and few models include sex and gender at all analytical stages—from data collection to sample balancing, feature engineering, and model training [59]. Hence, practical incorporation of sex- and gender-aware dimensioning into both patient data and their associated functional products remains an unaddressed challenge throughout the data-to-knowledge lifecycle.

Table 3 lists key molecular biomarkers that differ significantly by sex, illustrating their importance in prognostic and therapeutic decision-making. Incorporating such sex-specific variables improves accuracy in diagnosis and treatment approaches.

Table 3.

Sex-specific biomarkers and clinical relevance

Biomarker	Sex association	Cancer type	Clinical use	References
BRCA1	Female > Male	Breast/Ovarian	Risk prediction, targeted prevention	[9]
ERα (Estrogen receptor alpha)	Female	Breast	Target for hormonal therapy	[63]
PSA (Prostate-specific antigen)	Male only	Prostate	Screening biomarker	[37]
TP53 mutations	Varies by sex	Lung, Liver	Prognostic marker	[32], [61]
HER2 overexpression	Female > Male	Breast, Gastric	Target for trastuzumab	[24]
CTLA-4 expression	Female high	Melanoma	Immunotherapy predictor	[28]
AR (Androgen receptor)	Male	Prostate/Breast	Hormonal therapy guidance	[37]
KRAS mutation frequency	Higher in females	Colorectal	Prognostic indicator	[26]
PD-L1 expression	Variable	Lung	Immunotherapy selection	[46]
CYP3A4 enzyme activity	Female high	Multiple	Drug metabolism rate	[45]

Biological differences

“Sex” and “gender” are two variables crucial when developing predictive models for cancer diagnosis, treatment, and outcomes [24, 44]. Sex differences are variations in gene expression, metabolism, physiology, and other biological functions determined by chromosomal complement at conception [6]. Y-chromosome genes initiate a fetal hormonal cascade that normally causes testes to produce testosterone, and thus establish the phenotype, although the process is incompletely deterministic. Genetically female people do not develop recognizable reproductive organs and secondary sex characteristics, for example, if androgen signaling is inadvertently initiated. By contrast, “Gender” refers to a personal, internal perception of oneself as male, female, or a member of another category. This definition focuses on gender identity to reduce ambiguity [37]. Sex moderates gender,gender does not affect sex. Each moderates cancer risk, incidence, progression, survival, and treatment response.

Sex differences arise from sex chromosome complement, reproductive tissue differentiation, and activational effects at puberty of the hormonal milieu [6]. Gender differences result from the complex interactions of social, cultural, and economic forces and personal behaviors that influence the place of residence and work, diet, physical activity, exposure to environmental toxins, and access to health care. Tumors associated with sex-specific factors, such as those of the breast, prostate, ovary, or endometrium, commonly exhibit different mutational landscapes and differ concerning risk, incidence, response to treatment, and/or progression [28]. Cancer types that occur in both sexes but are not modulated by reproductive organs also show sex differences in risk, incidence, overall survival, progression, response to treatment, and/or mutational profile. Sex differences are usually greatest in midlife and diminish with advancing age, at least in part because plasma levels of androgens and estrogens in both sexes decline with age. Gender can influence cancer risk and cancer outcome by affecting exposure to carcinogens, diet, comorbidities, and the immune system, as well as the ability to accept a diagnosis and a course of treatment [59]. Interactions between sex and gender also influence many diseases, and cancer is no exception.

Figure 5 illustrates the clinical relevance of sex-specific biomarkers in oncology, highlighting a multi-modal workflow that spans from tumor sample analysis to molecular profiling. This integrative approach emphasizes how biological sex influences biomarker expression, shaping diagnostic accuracy, therapeutic targeting, and clinical outcome correlations.

Fig. 5.

Clinical relevance of sex-specific biomarkers in oncology: a multi-modal approach from sample analysis to outcome correlation

Social and behavioral factors

Cancer is a progressive disease that can be influenced by behavioral and social factors contributing to disparities in its diagnosis, therapy, and outcomes. Widely varying psychological stressors are differently affecting women and men worldwide [37]. Today, epidemiological, clinical, and pre-clinical research overwhelmingly confirms that cancer characteristics differ between sexes [24]. The etiology of sex- and gender-based differences is multifactorial, with pathways including genetic, epigenetic, hormonal, environmental, immune response, and microbiota factors [28]. Although the social constructs of gender and role expectations are frequently highlighted in health disparity literature, behavior associated with gender roles also influences cancer outcomes. For example, tobacco use, alcohol consumption, delaying seeking medical care, and willingness to engage in cancer screening programs are strongly linked to gendered social constructs [59]. In contrast, health information processing, health literacy, and receptiveness to behavioral change are considered less likely to represent consequences of gendered behavior.

AI-driven approaches to integrate sex and gender

Integrating sex and gender variables with other biomarkers constitutes a promising avenue for the development of robust, generalizable predictive models [2]. Sex and gender data may be collected either from patient records or by using natural language processing tools capable of mining such variables from clinical text. Similarly, omics datasets can be curated based on the sex of the sampled patients [50]. Subsequently, several machine learning strategies facilitate such integration. A strong a priori assumption, reflecting most prior work on sex- or gender-based differences, posits that females and males exhibit distinct correlations between a given biomarker and cancer incidence, severity, or treatment response. Four approaches that incorporate such assumptions at increasing levels of systemic complexity are discussed. First, sex or gender may be treated as an ordinary predictor of tumorigenesis or treatment response. Second, separate risk predictors may be trained independently for each sex or gender. Third, sex- or gender-specific models make explicit the prediction of interactomes that correlate with either biological sex or socially constructed gender roles. Finally, an optimal combined strategy utilizes a data-driven sub-stratification of patients to discover cancer subtypes definable in terms of molecular and clinical signatures that include sex and gender.

Data collection and analysis

As cancer tooling advances data capturing capabilities, sex remains one of the most robust classifiers to group patients. Machine learning models can integrate high-dimensional features, like those extracted from histopathological images, and identify biomarker panels predictive of clinical outcomes [2]. From a data collection and analysis standpoint, sex and gender variables must be specified at the same level of rigor as molecular and clinical covariates. In structured datasets, biological sex can be encoded as a binary or categorical variable with explicit handling of intersex and missing categories, whereas gender identity and gendered exposures (e.g., caregiving roles, occupational hazards) are captured using multi-category or continuous indicators rather than collapsed into a single ‘other’ group [2, 50]. To reduce confounding, sampling strategies and inclusion criteria should be designed to avoid strong correlations between sex or gender and basic clinical variables (such as tumor stage, treatment regimen, or center), and to ensure adequate representation of each stratum across training, validation, and test sets. In high-dimensional omics or imaging studies, feature extraction pipelines can be complemented by sex- and gender-stratified exploratory analyses to identify biomarkers whose distributions differ markedly between strata, so that downstream models can explicitly model, rather than inadvertently ignore, these systematic differences [50, 59]. Incorporating sex as an essential biological variable in conjunction with high-dimensional feature data holds promising potential. Revealing molecular markers associated specifically with males or females could steer the discovery of targeted therapies aligned with sex-specific cancer subtypes.

Model development

The development of predictive cancer models that incorporate sex and gender as precision biomarkers involves several key steps. First, datasets containing information on genomics, transcriptomics, proteomics, metabolomics, and clinical data are compiled. From these sources, sex- and gender-relevant variables that could function as precision biomarkers are extracted and identified. Two approaches then seek to discover predictive cancer models that integrate these factors. The first approach develops individualized classifier models that, if sufficiently accurate, can directly indicate a patient’s risk of developing a specific cancer type. The second approach generates representative generative models for each cancer type, combining these with classification systems to assess diagnostic probabilities. In both cases, selected variables are embedded within modeling architectures designed to produce cancer predictions. Models are subsequently refined and evaluated using a dedicated testing set, determining their accuracy and reliability. Several AI and ML paradigms permit the inclusion of sex and gender at the model-development stage, going beyond their use as straightforward covariates. In multi-task learning architectures, sex- or gender-specific output heads can be trained alongside a shared backbone to reduce negative transfer between strata and separate common prognostic structure from task-specific signals [72]. In order to allow effect sizes for important biomarkers to differ across strata while preserving a common representation for uncommon outcomes, stratification-aware models train either distinct models for each sex/gender group or hierarchical models with group-specific parameters. In order to maximize clinical utility while specifically keeping an eye on equity, fairness-aware algorithms also add restrictions or regularization terms that penalize systematic differences in error rates, calibration, or treatment recommendations between sex and gender groups [70, 72]. Rather than using sex as a proxy for several correlated pathways, these models must technically address collinearity between sex and other biomarkers (such as hormone receptor status or body composition), which can otherwise result in unstable parameter estimates or spurious variable importance. Solutions include careful feature selection, regularization, and explicit interaction modelling [32]. Likewise, in longitudinal or multi-sample settings, where repeated measures from the same individual could otherwise appear in multiple splits, rigorous patient separation across training, validation, and test folds is necessary to prevent data leakage when using stratified models or group-aware cross-validation schemes. Explicitly documenting these design choices is critical for ensuring that sex- and gender-informed models are both reproducible and robust [70]. Through this process, sex and gender are systematically incorporated as pivotal elements in the creation of precision models for cancer diagnosis, therapy selection, and outcome prediction [70].

Practical AI pipeline for sex- and gender-aware modeling

This subsection describes a useful AI pipeline that can be applied to oncology datasets, building on the conceptual strategies described above: using sex or gender as predictors, training sex-stratified models, modeling sex-/gender-specific interactomes, and data-driven sub-stratification [16]. In contrast to post hoc covariates, the intention is to guarantee that sex and gender are handled as essential design variables during the data curation, model development, validation, and deployment processes [23].

• Cohort definition and data curation

  The pipeline starts with a clear cohort definition that includes pre-established, uniformly applied inclusion and exclusion criteria for both sexes and genders. In addition to behavioral and psychosocial factors that record gendered exposures, key data sources usually include imaging, histopathology, structured electronic health records (EHR), and multi-omics [36, 59]. Harmonizing sex and gender annotations across sources is important in order to resolve discrepancies (such as those between self-reported gender identity and administrative sex-at-birth fields) and to encode missingness rather than tacitly excluding underrepresented groups explicitly.

• Representation and pre-processing of sex and gender

  When validated composite indices are available, there are several design options for encoding sex and gender: (a) categorical variables (e.g., male, female, intersex, non-binary), (b) continuous or ordinal scores, or (c) multi-hot encodings to represent complex identities and roles [34, 49]. Treatment of sex- and gender-related dimensions as continuous latent variables derived from clinical and behavioral data has also been proposed in recent work. This approach may preserve interpretability in downstream models while capturing gradations beyond binary categories [59]. Pre-processing procedures should evaluate class imbalance across sex/gender strata regardless of parameterization. Carefully apply techniques like stratified sampling, inverse-probability weighting, or synthetic minority over-sampling to maintain clinically meaningful distributions.

• Feature engineering and interaction modelling

  Interactions between sex and biological or clinical covariates, such as between sex and important biomarkers, treatment plans, or comorbidities, should be specifically taken into account during feature engineering. Both global and sex-stratified effects can be captured within a single framework by using hierarchical or multilevel models, which allow for sex-specific intercepts or slopes while representing shared biological mechanisms across sexes [6, 28]. Sex- and gender-specific heads that share lower-level representations but permit different task-specific parameters by sex/gender enable multi-task learning architectures to jointly model related prediction tasks (e.g., toxicity versus survival).

• Modelling alternatives and domain adaptation

  Regularized logistic regression or Cox models with sex/gender terms and important interaction terms provide transparency and clinical interpretation ease, making them a reasonable baseline for many cancer prediction tasks [32]. Especially when combining high-dimensional imaging, histopathology, or multi-omics data, more intricate models could be gradient-boosted decision trees, random forests, or deep neural networks [72]. When a model is trained in one healthcare setting and then implemented in another with a different sex/gender distribution, domain adaptation techniques become crucial because they enable re-weighting or representation alignment across institutions, regions, or time periods, preserving performance in underrepresented subgroups [28].

• Causal perspectives and sensitivity analyses

  Causal modeling is better than purely associational approaches when the goal is to comprehend how altering an exposure (such as a treatment, screening method, or behavioral intervention) would alter outcomes differently by sex or gender [37]). Gender and sex can be explicitly represented as moderators or confounders in causal frameworks (e.g., structural causal models or potential outcomes), which can differentiate between pathways mediated by gendered social determinants and direct biological effects [73]. The robustness of estimated sex- and gender-specific effects must be assessed using sensitivity analyses that vary modeling assumptions (e.g., alternative confounder sets, different encodings of sex/gender) [65].

• Training, internal validation, and hyperparameter selection

  To prevent performance estimates from being skewed toward majority groups, stratified cross-validation, which maintains the distribution of sex and gender within each fold, should be used in model development [2]. When choosing hyperparameters, nested cross-validation or distinct tuning and evaluation splits should be employed, and performance should be tracked both generally and within subgroups based on sex and gender [32]. The incremental value of completely separate versus shared modeling strategies can be measured by training separate models within each sex or gender category and comparing them with pooled models that include sex/gender and interaction terms, when sample sizes allow.

• Clinical integration and decision support

  Incorporating prediction models into clinical workflows—for instance, as decision-support tools that identify high-risk patients for additional screening or customized treatment—is the last step in the pipeline. In this situation, models that take into account sex and gender must be assessed not only for their statistical performance but also for their clinical utility, or whether they alter choices in ways that significantly enhance results and lessen disparities between sex and gender groups [8, 60]. In order to be accepted by clinicians and used appropriately, human–AI interaction design must include sex-/gender-specific risk explanations and intuitive visualization of subgroup performance.

Evaluation, validation, and monitoring

Evaluation of sex- and gender-aware AI systems must go beyond global accuracy metrics to include subgroup performance, fairness criteria, and real-world robustness in order to guarantee that these systems enhance rather than worsen disparities [30]. In addition to advice on external validation and drift monitoring, Table 4 provides a summary of suggested metrics and validation techniques across the discrimination, calibration, clinical utility, and fairness dimensions [72].

Table 4.

Recommended metrics and validation strategies for sex- and gender-aware AI models [30, 34, 50, 60, 72]

Dimension	Metric/strategy	Purpose in sex- and gender-aware modeling
Discrimination	Overall AUC (ROC, PR)	Quantify global ability to distinguish outcomes; baseline for comparison with sex-/gender-stratified AUC
Discrimination	Sex-/gender-stratified AUC, sensitivity, specificity	Reveal performance differences between groups that aggregate metrics may obscure
Calibration	Calibration plots; calibration-in-the-large; Brier score	Assess whether predicted risks match observed event rates overall and within each sex/gender subgroup
Calibration	Subgroup calibration error (e.g., expected calibration error per group)	Detect over- or underestimation of risk for specific sex/gender categories that could distort clinical decisions
Clinical utility	Decision curve analysis and net benefit (overall and by sex/gender)	Evaluate whether using the model improves decision-making compared with treat-all or treat-none strategies in each group
Clinical utility	Number needed to evaluate/impact on resource use by subgroup	Quantify workload and potential overdiagnosis/undertreatment in different sex/gender strata
Fairness/subgroup metrics	Equalized odds (difference in TPR/FPR between groups)	Identify systematic disparities in false positives or false negatives across sex and gender groups
Fairness/subgroup metrics	Equal opportunity; demographic parity (where appropriate); subgroup calibration	Characterize fairness from multiple perspectives (sensitivity, prediction rates, and calibration) across subgroups
Fairness mitigation	Reweighting; stratified sampling; constrained thresholding; adversarial debiasing	Adjust training or decision thresholds to reduce identified unfairness while monitoring impact on clinical validity
Validation robustness	Temporal validation (train–test splits by calendar time)	Test stability as treatments, diagnostics, and population risk factors change over time
Validation robustness	External and geographic validation (different centers/regions)	Assess generalizability when sex/gender distributions and clinical practices differ across settings
Validation robustness	Sensitivity analyses (alternative encodings of sex/gender, alternative confounder sets)	Evaluate the robustness of conclusions to modeling choices about sex/gender representation and adjustment
Monitoring and drift	Ongoing tracking of AUC, calibration, and fairness metrics by sex/gender in production	Detect degradation in subgroup performance after deployment
Monitoring and drift	Data and concept drift detection (e.g., changes in feature distributions or outcome incidence by sex/gender)	Identify when re-training, recalibration, or model retirement is required to maintain safe and equitable performance

A minimal assessment battery for cancer prediction models should include information on clinical utility (e.g., decision curve analysis and net benefit at clinically relevant thresholds), calibration (e.g., calibration curves, Brier score, and calibration-in-the-large), and discrimination (e.g., overall and sex-stratified area under the receiver operating characteristic curve [AUC] [23], sensitivity, specificity, and precision–recall metrics). To show whether performance differences could exacerbate or lessen current disparities in incidence, treatment access, or outcomes, subgroup metrics such as sex-stratified AUC, calibration error, and net benefit curves should be provided for each sex and gender category [32].

Furthermore, it is possible to measure whether prediction errors are distributed differently among sex and gender groups using explicit fairness metrics [34]. Examples include subgroup calibration (determining whether predicted risks correspond to observed event rates within each sex/gender category), equal opportunity (comparing sensitivity among truly positive patients), and equalized odds (comparing true- and false-positive rates between groups). Mitigation techniques like reweighting, constrained optimization (e.g., imposing equalized-odds constraints), threshold adjustments per subgroup, or adversarial debiasing can be taken into consideration when significant unfairness is identified [44]. However, any correction must be interpreted in the context of underlying epidemiology and care pathways rather than just statistical symmetry.

Models must be tested on datasets other than the development dataset for robust validation. While geographic or institutional validation measures performance when sex and gender distributions vary across centers or regions, temporal validation (e.g., training on prior years and testing on subsequent years) evaluates stability as treatment protocols and population risk factors change [50]. To stress-test generalizability, external validation cohorts should ideally include sites with consistently different sex/gender compositions and care patterns. When possible, pre-planned prospective or pragmatic trials can assess whether making clinical decisions based on sex- and gender-aware predictions improves outcomes, decreases adverse events, or closes survival gaps [61].

Because clinical procedures and data distributions change over time, sometimes in ways that are specific to a given sex or gender, post-deployment monitoring is crucial [30]. Changes in input feature distributions (data drift), the relationship between predictors and outcomes (concept drift), and discrimination and calibration metrics, both generally and within sex/gender subgroups, should all be monitored through ongoing or sporadic performance surveillance (Table 4). When any subgroup’s performance drops below predetermined safety thresholds, monitoring dashboards can notify users, causing the model to be re-trained, recalibrated, or rolled back [60]. When models are updated, and clinicians adjust to their recommendations, audits should also look at whether downstream decisions (such as screening referrals or treatment intensification) continue to be equitable across sex and gender categories.

Case studies: AI applications in cancer

AI methodologies assist in the identification, analysis, and treatment of different cancers, frequently employing approaches similar to those that characterize sex and gender characteristics. The most recent studies of AI-assisted solutions analyse breast cancer, prostate cancer, and lung cancer [61, 69].

Breast cancer is the most commonly diagnosed malignancy in women, representing 30% of cancer cases worldwide. Early diagnosis and treatment, as well as individualized therapies, are critical to ensuring the survival of these patients, and the integration of specific individual variables (such as age and sex) improves the precision of AI-based tools for data analysis and prognosis in cancer patients [5]. Recent work has demonstrated how sex-aware AI models can refine prognostic stratification in breast cancer [72]. In a large retrospective cohort of 22,176 patients treated at Fudan University Shanghai Cancer Center, machine-learning methods including random survival forests (RSF), Cox regression (with and without elastic net regularisation), and support vector machines were trained on 21 clinicopathologic variables—such as age, tumour size, nodal status, and hormone-receptor profiles—to predict overall survival [5]. The RSF model achieved the best discrimination, with a concordance index of 0.827 and time-dependent AUCs of 0.857, 0.838, and 0.781 at 3, 5, and 10 years, respectively, significantly outperforming Cox-based models and support vector machines and enabling more granular risk stratification among predominantly female patients.

Complementary studies have focused specifically on women undergoing breast cancer surgery, developing postoperative mortality and recurrence prediction tools using machine-learning algorithms trained on sex-homogeneous cohorts [14, 61]. These models integrate perioperative variables (e.g., comorbidities, operative factors) and tumour characteristics in female patients to construct web-based risk calculators that support personalised follow-up schedules and adjuvant therapy planning, thereby exemplifying how sex-specific AI prognostic tools can operationalise sex as a precision biomarker in breast oncology.

Sex-specific prostate cancer represents 15.2% of male tumour cases. A wide range of predictors influence patients’ prognosis, and AI-assisted tools integrate information about treatment, drugs, drug-related risks, comorbidities, and health status for clinical decision support in patients with prostate cancer and cardiovascular diseases [54]. The inclusion of population-specific factors in each case is essential to formulating patient-centred individual models that improve precision and limit generalizations Kristina [4].

Lung cancer constitutes 12.3% of overall cancer diagnoses. Its epidemiology is highly dependent on specific sex-related variables that strongly influence the development and growth of tumour cells, which can be translated into yet more specific therapeutic approaches. The inclusion of sex-related elements at the core of AI approaches, along with other clinical and biochemical features, allows for highly precise tools for supporting clinical decisions in patients with lung cancer [46].

Breast cancer

Breast cancer is the most common cancer affecting women worldwide, with approximately two million new cases annually, and is the second most common cancer overall [5]. Treatments have improved over the last four decades, encompassing surgery, chemotherapy, radiation, hormone therapy, immunotherapy, and targeted approaches. Tumor heterogeneity and population diversity result in extensive patient-to-patient variation in response to even the most effective treatments. Sex and gender provide an important basis to understand this heterogeneity, and multiple studies highlight the need to integrate these variables into prognostic and predictive models for cancer research, treatment, and prevention [61].

Prostate cancer

Prostate cancer is the most frequently diagnosed malignancy and the second leading cause of cancer mortality among U.S. men. Early detection and improved treatment selection strategies will reduce the mortality rate, but that goal must be balanced with a decrease in overdiagnosis and overtreatment. Precision medicine approaches focus on individual variability at distinct biological levels and are increasingly applied to prostate cancer management [54]. Although prostate-specific antigen screening is discouraged for the general population, precision medicine techniques hold promise for helping clinicians improve the accuracy of the diagnostic process, select more effective treatment modalities, and guide the management of metastatic disease (Kristina [4]). Current precision medicine tests—already available in clinical practice—address a wide spectrum of treatment scenarios, including indications for biopsy or rebiopsy; curative therapies versus active surveillance; adjuvant therapies after surgical resection; and selection for secondary hormonal therapies in cases of metastatic, castration-resistant prostate cancer [54]. As a male-specific malignancy, prostate cancer provides a natural context for sex-tailored AI models trained in sex-homogeneous cohorts [54]. The “Dr Answer” AI clinical decision-support system, for example, was developed using electronic records from 7,128 men treated with radical prostatectomy at three hospitals and employs random forest and k-nearest neighbours algorithms to predict key pathological outcomes, including TNM stage, extracapsular extension, seminal vesicle invasion, and lymph-node metastasis [4]. In internal validation, random forests achieved a recall of 76.98% for TNM staging. In contrast, k-nearest neighbours yielded recalls of 80.24%, 98.67%, and 95.45% for extracapsular extension, seminal vesicle invasion, and lymph-node metastasis, respectively, thereby providing urologists with individualised pre-operative risk estimates tailored to male patients.

Digital pathology–based AI biomarkers have further extended sex-specific precision in prostate cancer. A systematic review [54] of DP-AI in urologic malignancies summarised evidence from ten studies (n = 8,951 men) using the ArteraAI model, including two analyses totalling 2,786 men in which the model identified radiotherapy-treated patients who could safely omit short-term or long-term androgen-deprivation therapy, with subdistribution hazard ratios of 0.34 (95% CI 0.19–0.63) and 0.55 (95% CI 0.41–0.73) for failure events in selected subgroups (Kristina Angeles et al., 20,180. These results illustrate how sex-specific AI models, trained exclusively in male populations, can enable treatment de-escalation while preserving oncologic control and reducing therapy-related toxicity.

Lung cancer

Lung cancer is the second most prevalent cancer in men and the third in women worldwide, and the leading cause of cancer-related mortality for both sexes. Its death toll in women exceeds the combined sums for breast, ovarian, and uterine cancers. The designation of male and female refers to biological sex rather than gender identity. Initial treatment decisions for non-small-cell lung cancer incorporate variables such as stage, age, performance status, histology, PD-L1 expression, and the presence of oncogenic drivers; nevertheless, sex remains unaddressed.

The European Society for Medical Oncology underscores the imperative of sex awareness for treatment optimization. Since 2014, the NIH has recommended regard for sex as a biological variable in lung cancer investigations. Sex influences cancer presentation and interacts with risk factors such as smoking and radiation exposure, and sex-specific contrasts characterize therapeutic strategies and screening [46].

Heightened understanding of sex-specific lung cancer patterns underlies nuanced appraisal of sex-dependent responses to immunotherapy, while recognizing that gendered behaviors (such as smoking patterns and health-care access) further modulate treatment response and outcomes [46, 62]. Men face nearly twice the mortality risk across all cancers relative to women, with pronounced disparities in lung, melanoma, larynx, esophagus, and bladder cancers. Sex differences in lung cancer derive from biological, environmental, and hormonal factors, as well as immune responsiveness, whereas gender-related factors (for example, occupational exposures, tobacco use norms, and screening behaviors) further shape risk, presentation, and prognosis [22]. The immune system governs cancer progression and therapeutic efficacy, and sex-related divergences have been documented. Female cohorts remain consistently underrepresented, yet integration of both sexes is essential for elucidating gender impact and fostering sex-based diagnostic and treatment paradigms. Updated analyses highlight sex-based disparities in immune regulation and lung cancer immunotherapeutic outcomes [62]. Recent AI-based survival models for non–small cell lung cancer (NSCLC) explicitly integrate gender alongside other clinical and post-treatment variables, demonstrating that sex-related covariates can contribute meaningfully to risk stratification [35]. In an analysis of the TCGA lung adenocarcinoma (LUAD) cohort, Cox proportional hazards (CPH) models and random survival forests (RSF) were trained using baseline and post-treatment predictors, including age, gender, progression-free interval, and residual tumour status (Raskin et al., 20,220. Gender emerged as a significant prognostic factor in the CPH model, with males exhibiting approximately 20% lower hazard than females (hazard ratio 0.80, p = 0.01), and the full multivariable CPH model achieved a concordance index of 0.90, matching or exceeding the best performance reported in earlier lung cancer survival studies.

These findings illustrate how incorporating gender into multivariable AI models can uncover sex-associated differences in long-term survival, improving model discrimination and informing more nuanced counselling about prognosis and follow-up [73]. More broadly, such approaches exemplify how sex and gender can be embedded as structured predictors in AI pipelines for lung cancer, complementing image-based deep-learning models that predict response to immune checkpoint inhibitors and enhancing the design of treatment strategies that are sensitive to sex-related differences in immune regulation [62].

Collectively, these case studies in breast, prostate, and lung cancer show that AI systems can move beyond sex- and gender-neutral prediction to explicitly leverage sex-specific cohorts, sex/gender-stratified variables, and sex-related response patterns [61]. By embedding these dimensions within diverse modelling frameworks—from random survival forests and Cox models to digital pathology networks and clinical decision-support tools—AI can refine risk stratification, guide treatment selection, and support de-escalation strategies that systematically account for sex and gender as precision biomarkers [35, 73].

Therapeutic implications of sex and gender

Malignant tumors have demonstrated sex-biased outcomes [24]. At the same time, men present a higher incidence of lung, kidney, or bladder cancer, whereas the incidence of thyroid and gallbladder cancers is higher among women [10, 25]. Emerging evidence supports the role of the patient’s sex and gender to guide cancer diagnosis and therapy, and sex- and gender-specific adverse drug reaction profiles have long been noted for cancer drugs, as mentioned in Fig. 6 [51]. However, current clinical practice does not fully acknowledge the importance of sex- and gender-specific precision medicine: the majority of currently approved cancer drugs have been registered through trials conducted on mixed patient populations and do not consider sex and gender differences during treatment allocation [74]. Integrating sex and gender as precision biomarkers in AI-driven diagnostic, therapy, and predictive analytics systems will lead to a more accurate understanding of individual risk profiles and cancer progression dynamics, thereby enabling the design of personalized treatment strategies [41].

Fig. 6.

A conceptual framework of the therapeutic implications of sex-specific and gender-related factors in cancer care

Personalized treatment strategies

In the context of cancer therapeutics, biological sex and gender emerge as fundamental determinants of individual response, underscoring the need for personalized treatment approaches. AI-driven technologies facilitate the development of predictive models that integrate sex- and gender-specific biomarkers with established cancer diagnostics [14]. Personalized medicine aims to account for variability in genes, environment, and lifestyle—variables heavily influenced by sex and gender—to tailor prevention, diagnosis, treatment, recovery, and palliation strategies [14]. Incorporating these dimensions into drug discovery pipelines, AI analytics, and clinical decision-making hinges on access to comprehensive, representative datasets. This crucial consideration complements the modeling and analytical frameworks detailed in earlier sections, where the impact of sex- and gender-related predictors on drug toxicity and outcome measures can be rigorously quantified [41].

Adverse drug reactions

Adverse drug reactions (ADRs) have a profound impact on patients receiving chemotherapy, as numerous treatments exhibit heightened toxicity in women compared to men [51]. Ensuring the safety of pharmaceuticals is paramount during clinical trials. Data monitoring committees and an independent data monitoring committee are essential for ensuring patient safety, upholding scientific rigor, and protecting data confidentiality through the exchange of interim safety data, including adverse event rates [52]. Variations in sex and gender impact how well individuals stick to their medication regimens, potentially linked to differing profiles of adverse drug reactions [52]. Clinical outcomes that differ by sex contribute notably to the variability seen in individual responses to drugs and their associated toxicity [74]. More than 80% of active compounds undergo metabolism via cytochrome P450 enzymes, which exhibit differential regulation between genders [73]. The variations observed play a significant role in the differing drug-drug interactions between sexes, exemplified by the findings related to zolpidem [19]. Differences in pharmacokinetics and tissue distribution between genders also influence the varying drug responses observed in each sex [56]. Therefore, it is essential to methodically take into account the diversity of adverse drug reaction risk factors, which encompasses extensive, varied study populations and contemporary clinical trial methodologies. Adaptive clinical trial designs tackle this issue by permitting adjustments informed by the data that is continuously gathered. Therapeutic drug monitoring provides a valuable strategy to reduce adverse drug reactions while minimizing extra risks. Integrating suitable sex-stratified analyses throughout all phases of therapeutic development minimizes the occurrence of adverse drug reactions [74]. The study of pharmacogenomics, which focuses on the genetic foundations of personalized drug responses, is likely interconnected with variations in sex and gender regarding pharmacokinetics and pharmacodynamics. This link signifies an important research pathway for improving safety and treatment results in precision medicine [17]

Outcome prediction models

Supervised machine-learning algorithms have become increasingly favored for forecasting a patient’s clinical outcomes using molecular data, potentially guiding prognostic evaluations and treatment strategies. Nevertheless, when it comes to classifying patients based on biomarkers, these approaches fail to offer a confidence level that signifies the reliability of predictions for individual patients. This shortcoming presents difficulties in assessing the relevance and practical use of biomarkers in clinical settings. To tackle these challenges, Xu et al. [68] introduced a framework that includes two complementary models—the Cancer Outcome Prediction Model (CPM) and the Prediction Confidence Probability Model (PPM)—which together facilitate more precise outcome prediction and evaluation of prediction reliability. The CPM forecasts patient survival directly from molecular data through machine-learning techniques like random forests or regression models, whereas the PPM assesses the confidence level of the CPM’s predictions based on molecular profiles or clinical variables.

When a biomarker score exceeds a set threshold, the PPM produces a confidence measure for the related outcome prediction, with elevated scores signifying a greater relevance of the biomarker for the specific patient. This dual-model strategy not only boosts predictive accuracy but also aids in pinpointing patient subsets where a biomarker holds the greatest significance, thus enhancing biomarker utility and fostering more tailored clinical decision-making [68]. Through the collaborative development of both models, the framework addresses inaccuracies stemming from tumor diversity and aids in the identification of suitable candidates for clinical trials, thereby enhancing the precision of evaluations and facilitating the effective application of biomarkers and targeted therapies.

Survival analysis

Sex and gender as precision biomarkers have gained attention in cancer diagnosis, therapy, prediction, and prognosis. The complex interplay of factors between and within these dimensions has become amenable to analysis by artificial intelligence (AI) and machine learning techniques, which ensure precise and effective disentanglement of the complex interactions between sex/gender-related variables (biological, clinical, psychosocial, and socio-behavioral) and the cancer data [47]. Cancers appear to respond to sex- and gender-related factors with recognizable characteristics, such as survival probability. While the influence of sex and gender on cancer risk and tumor biology is well-established, the impact of gender on survival remains underexplored. Many studies investigating sex-based survival differences fail to account for confounding factors, thereby limiting the accuracy of conclusions. This study addresses the oversight by matching variables to more accurately discern the influence of sex and gender on survival outcomes. Using state-of-the-art AI and computer-aided approaches, it identifies the most important sex- and gender-related candidate biomarkers and provides robust evidence for their predictivity, thus assisting responsible, effective AI-based treatment [47].

Quality of life assessments

Quality of life (QoL) is a major consideration in cancer treatment. As integrated QoL definitions gain traction and instruments for psychosocial measurement become more refined, researchers seek to develop sex- and gender-specific frameworks for the distinct QoL challenges faced by those with cancer. Differences in QoL have been observed across cancer types and disease stages. Although some QoL domains may undergo parallel evolution between males and females, certain subsets show statistically significant divergence. For example, a prospective investigation found that patients with non-small cell lung cancer suffered comparable QoL disruptions at diagnosis, yet women—at both local and advanced stages—consistently registered poorer QoL than men [29]. Furthermore, oncology patients report higher rates of psychological comorbidities such as depression and anxiety in contrast to non-cancer individuals [39]. In breast cancer survivors (BCS), age, treatment modality, and elapsed time since first intervention inform QoL appraisal. Utilization of age-adjusted prospective data instead of relying solely on cross-sectional reporting enhances the accuracy of QoL estimation [3]. Capitalizing on the distinct trajectories by which QoL measures evolve as a function of sex and gender, AI-driven assessment tools can prognosticate the QoL implications associated with diverse therapeutic strategies, thereby underpinning sex- and gender-aware intervention frameworks. QoL represents just one outcome—among several candidates—that may benefit from subgroup differentiation in precision cancer initiatives.

Challenges in implementation

The application of AI and sex and gender as precision biomarkers in cancer diagnosis, therapy, and prediction is frequently impeded by data quality and ethical concerns. AI’s predictive power depends on the volume and representativeness of data. If data exhibit sparse coverage or structural imbalances that link sex or gender to features, labels, or clinical outcomes, then modelling systems are likely to produce biased and unreliable predictions. In particular, any such biases could be amplified in AI architectures composed of many layers, each performing nonlinear mixtures of the inputs [30]. A contributing factor is the under-representation of female patients in publicly available datasets, a reflection of the lower recruitment of women in clinical trials and their general exclusion in pre-clinical animal testing and cell-line experiments [36]. AI systems trained on such data have a limited ability to represent demographic groups with small sample sizes and can produce unreliable results.

More broadly, substantial uncertainties remain about the mechanisms by which sex and gender influence the pathophysiology and outcomes of cancer. Whereas several causal links governing sex differences are well-defined (for example, the influence of hormone-receptor signalling on gene expression and pathways), corresponding information about gender-related mechanisms is significantly sparser [37]. Some AI architectures provide an opportunity to partially offset this shortcoming by learning latent, nonlinear associations directly between gender and outcomes, although the results obtained by such approaches are inherently correlative and remain agnostic regarding the underlying causes.

An additional difficulty is that sex and gender may fail to serve as the best predictor variables in some scenarios of a particular demographic factor. A case in point concerns investigations of immunotherapies in non-small-cell lung cancer. Although initial studies suggested that women exhibit a superior response to checkpoint inhibitors, subsequent meta-analyses have produced contradictory conclusions depending on which checkpoint inhibitor is used [7]. These discrepancies may arise from underlying correlations with other demographic and behavioural variables and highlight an intrinsic limitation of analyses relying on sex or gender. For those applications in which the determining variables have already been identified, linking the outcome to sex or gender alone provides only a rough approximation.

As a consequence, a more reliable predictive strategy consists of the identification and application of the causal variables themselves [44]. Table 5 summarizes the main barriers and opportunities in incorporating sex and gender variables into AI-driven oncology. Sex-related covariates can significantly contribute to risk stratification, as evidenced by recent AI-based survival models for non-small cell lung cancer (NSCLC) that explicitly incorporate gender along with other clinical and post-treatment variables [35]. Cox proportional hazards (CPH) models and random survival forests (RSF) were trained using baseline and post-treatment predictors, such as age, gender, progression-free interval, and residual tumor status, in an analysis of the TCGA lung adenocarcinoma (LUAD) cohort [46]. With men showing about 20% lower hazard than women (hazard ratio 0.80, p = 0.01), gender emerged as a significant prognostic factor in the CPH model. The full multivariable CPH model also achieved a concordance index of 0.90, which matched or exceeded the best performance documented in previous lung cancer survival studies [46, 73].

Table 5.

Challenges and opportunities in implementing sex/gender-based AI models

Challenge/opportunity	Category	Impact	Example	Potential solution
Data imbalance	Challenge	Skewed model outputs	Male-biased clinical datasets	Balanced sampling
Annotation gaps	Challenge	Missing sex/gender labels	EHR without gender identity	Retrospective labeling
Regulatory compliance	Challenge	GDPR restrictions	Limited access to sensitive data	De-identified linked datasets
Cultural sensitivity	Challenge	Misinterpretation of gender factors	Ignoring nonbinary identities	Inclusive variable encoding
Multimodal integration	Opportunity	Richer data context	Combining genomics + lifestyle	AI fusion models
Sex-specific therapy targets	Opportunity	Personalized treatment	Hormonal pathways	Target discovery pipelines
Real-time monitoring	Opportunity	Adaptive therapy	Wearable oncology monitoring	ML-driven feedback loops
Public health impact	Opportunity	Better cancer prevention	Gender-targeted screening	AI-guided awareness programs
Cross-disciplinary collaboration	Opportunity	Holistic research	Oncologists + data scientists	Joint consortia
Education & training	Opportunity	Skilled workforce	AI in clinical oncology education	Dedicated curricula

These results demonstrate how adding gender to multivariable AI models can reveal sex-related variations in long-term survival, enhancing model discrimination and guiding more sophisticated prognostic and follow-up counselling [62]. More generally, such methods demonstrate how gender and sex can be incorporated as structured predictors into AI pipelines for lung cancer, enhancing the development of treatment plans that take into account variations in immune regulation related to sex and enhancing image-based deep-learning models that forecast response to immune checkpoint inhibitors [23].

Together, these case studies in lung, prostate, and breast cancer demonstrate that AI systems are capable of explicitly utilizing sex-specific cohorts, sex/gender-stratified variables, and sex-related response patterns, going beyond sex- and gender-neutral prediction [61]. AI can improve risk stratification, direct treatment choices, and assist de-escalation tactics that methodically take sex and gender into account as precision biomarkers by integrating these dimensions into a variety of modeling frameworks, such as random survival forests, Cox models, digital pathology networks, and clinical decision-support tools [5].

As detailed in Table 5, data imbalance and annotation gaps represent foundational threats to model validity: male-biased or otherwise skewed datasets, combined with missing sex and gender labels, can cause AI systems to underfit underrepresented groups and to misestimate risks and benefits for those patients [34, 64]. Addressing these issues in practice requires a combination of design-stage strategies (such as targeted recruitment and stratified sampling), algorithmic interventions (for example, re-weighting or bias-mitigation constraints), and rigorous, subgroup-stratified performance evaluation. Regulatory compliance and cultural sensitivity further complicate these efforts, as privacy regulations and institutional policies may limit access to granular gender-identity data, and oversimplified encodings risk erasing non-binary and transgender populations [18]. Consequently, inclusive variable encoding, community and stakeholder engagement, and privacy-preserving data-linkage approaches are essential to ensure that expanded representation does not translate into new forms of surveillance or harm.

At the same time, the opportunity categories in Table 5 underscore how multimodal integration, sex-specific therapy targets, real-time monitoring, public health impact, and cross-disciplinary collaboration can transform these challenges into drivers of equitable precision oncology. For example, combining genomic, imaging, lifestyle, and behavioral data in sex- and gender-aware fusion models can reveal subgroup-specific therapeutic targets and toxicity profiles that are invisible in aggregate analyses [60]. Wearable sensors and longitudinal patient-reported outcomes enable real-time, sex- and gender-stratified monitoring of treatment response and quality of life, informing adaptive treatment strategies [29]. Finally, sustained collaboration among oncologists, data scientists, ethicists, patients, and policymakers—supported by dedicated education and training initiatives—will be necessary to operationalize these opportunities and to ensure that sex- and gender-aware AI tools narrow, rather than widen, existing cancer disparities [20].

Data bias and representation

Sex-based disparities are pervasive in research, especially health-related and pre-clinical studies [44]. Male pre-clinical models are predominantly preferred to minimize inter-individual variation. The downstream effects on clinical and translational work are pronounced, perpetuating and intensifying a systemic undertreatment of females and women. Similar biases are present in medical research—only 4–8% of studies systematically analyze results by sex/gender [34]. Moreover, women and females remain underrepresented in clinical trials, with a lag of nearly two decades between the number of males and females being investigated [24].

Regarding cancer, several risk factors exhibit significant sex and gender differences. For instance, smoking is more prevalent among males, but females have an increased lung cancer risk due to higher sensitivity to toxins [24]. Variations also exist in diet and physical activity, alcohol consumption, occupational exposures, and body and fat mass and distribution, which tend to differ between males and females. The high-dimensional nature of sex and gender, combined with widespread bias and underrepresentation within the biomedical literature, must be carefully considered when engaging in sex- and gender-informed data curation and pre-processing. In this context, constructing “gender-balanced” datasets cannot be reduced to achieving a 50:50 distribution of male and female participants. Instead, dataset design must explicitly account for intersectionality, ensuring that sex and gender subgroups are adequately represented across relevant axes such as race and ethnicity, socioeconomic status, geography, comorbidities, and exposure histories [26, 34]. From a technical perspective, this implies moving beyond naive sample balancing toward stratified recruitment and sampling strategies, subgroup-specific oversampling or re-weighting, and fairness-aware learning objectives that constrain performance disparities across intersecting subpopulations. Conceptually, these measures recognize that marginalized groups (for example, women from lower socioeconomic backgrounds or gender-diverse individuals from racialized communities) may experience distinct cancer risks, care pathways, and outcome profiles that will be systematically misrepresented if sex and gender are treated as isolated binary covariates [26, 59].

Moreover, gender identity itself must be represented with greater granularity than a simple male–female dichotomy. Prospective data collection instruments should distinguish at a minimum between sex assigned at birth, current gender identity, and intersex variations, and they should include inclusive response options for transgender and non-binary individuals, guided by evolving clinical documentation standards [18]. Retrospectively, existing datasets often encode sex and gender inconsistently or conflate these constructs with administrative “sex at birth” or insurance categories,careful curation therefore requires transparent mapping of legacy codes, documentation of uncertainty, and avoidance of heuristic recoding that could erase gender-diverse identities [49]. Only by embedding such nuanced constructs into the data model can AI systems begin to capture how sex and gender interact with structural inequities to shape cancer trajectories [36].

A further challenge arises when harmonizing sex- and gender-related information across heterogeneous data sources, including electronic health records, genomic and imaging repositories, disease registries, and clinical trial databases. Each source may encode sex and gender differently, with variable use of free text, structured fields, legacy coding schemes, or implicit assumptions [30, 50]. Technically robust harmonization demands standardized terminologies and ontologies for sex and gender variables, explicit mapping of disparate coding systems, and systematic handling of missingness and discordant entries. Natural language processing may assist in extracting sex- and gender-relevant information from unstructured clinical notes, but such derived variables must be clearly flagged, versioned, and validated against gold-standard annotations to avoid propagating misclassification into downstream models [30].

Both retrospective and prospective workflows, therefore, require explicit annotation and validation pipelines for sex and gender variables [18]. Retrospective curation should include rule-based and machine-learning checks for internal consistency (for example, between sex-coded fields, reproductive history, and hormone therapy records), auditable correction procedures for detected errors, and governance mechanisms to restrict re-identification risks when working with sensitive gender-identity information [64]. Prospectively, study protocols and EHR templates should mandate standardized fields for sex assigned at birth, current gender identity, and, where appropriate, intersex status, aligned with regulations such as GDPR and institutional ethics guidance. Embedding these standards into case report forms, FHIR/OMOP-based data models, and data-sharing agreements facilitates reproducible, multi-institutional AI development while respecting patient autonomy, privacy, and the right to self-identification [59].

Ethical considerations

The incorporation of sex and gender as precise indicators in cancer diagnosis and treatment raises ethical dilemmas that need to be tackled to guarantee fair and accurate use of artificial intelligence and machine-learning technologies. Ethical considerations mainly focus on the dangers of data bias, misrepresentation, and the possibility of misuse stemming from insufficient or biased data collection methods [34]. Models developed using incomplete, biased, or unrepresentative datasets are prone to producing erroneous conclusions, which can reinforce existing inequalities in cancer treatment and hinder the objectives of tailored oncology. Healthcare institutions bear a crucial duty in the ethical oversight of sex- and gender-specific data, making certain that collection methods accurately represent relevant profiles and appropriately reflect the diversity of the population [18]. Considering the extensive implications of these variables—spanning chromosomal determination, phenotypic expressions, social roles, behaviors, and identities—it is essential to establish precise definitions and systematic distinctions within electronic health records (EHRs) standards. Establishing clear and widely recognized definitions is crucial for supporting strong and dependable analytical frameworks.

Furthermore, the responsible management of sex- and gender-related information in oncology requires addressing institutional obstacles that hinder the recognition of these identities and impede data gathering. At the same time, it is essential to tackle socio-cultural biases and consider changes across different periods and contexts. Neglecting to address these challenges may lead to the continuation of biases in AI-driven systems, undermining initiatives aimed at diminishing disparities in cancer diagnosis and treatment results.

Future directions in research

New and artificial intelligence-based methods offer unprecedented opportunities for integrating sex and gender as biomarkers in cancer diagnosis, therapy, and outcome prediction. Innovative approaches are expected to accelerate understanding and implementation significantly. Wide prospective longitudinal studies are essential to address persistent scientific gaps, optimize AI-based solutions, and achieve precision oncology where sex and gender are key components.

Innovative AI techniques

Building upon the deepening understanding of cancer genomics, artificial intelligence (AI) is increasingly used to integrate sex or gender information into precision oncology. AI leverages diverse data types—clinical variables, imaging, omics, histopathology, and more—to advance approaches that delineate the influence of sex and gender as part of AI-driven cancer-therapy and outcome prediction, highlighting innovative directions for future research [35, 61].

Longitudinal studies

Longitudinal studies provide insight into trends in sex- and gender-based factors that affect cancer diagnosis, therapy, and prediction. Such cancer trends have been observed as far back as 2024 [7]. There remains a fundamental need for additional longitudinal surveys that collect key information about sex and gender. For example, sex- and gender-stratified risk factor data would allow researchers to track changes in incidence or mortality associated with downstream effects of tobacco smoking, alcohol use, recommendation or implementation of specific screening programs, or other relevant factors. Monitoring trends in sex- and gender-stratified drug prescription rates could reveal preventative or potential therapeutic uses of approved drugs and naturally occurs alongside the simulation and prediction of sex- and gender-specific ADRs across patients [72]. Monitoring sex- and gender-stratified trends in quality-of-life determinants could facilitate the design of societal intervention programs that target cancer survivors.

Longitudinal studies have the potential to inform the implementation of methods, policies, and social-awareness initiatives focused on sex and gender in cancer prevention, detection, therapy, prognosis, and monitoring. Most traditional longitudinal studies, however, require substantial time and are often limited to a few dozen or several hundred participants; thus, acquiring this human participant information quickly, on a large scale, and as additional reported detail or measure is difficult. Integrated mathematical approaches complement the longitudinal study design, allowing for the inference of key sex- and gender-influenced population information even without relying on longitudinal input data. Such factors include the determination of representative sex- and gender-stratified state- or county-level cancer screening rates by age group, extraction of sex- and gender-specific quantitative survival times and quality-of-life determinants from time-dependent survey data, or designation of the relevance of sex- and gender-influenced conditions based on diagnostic codes and temporal ordering.

Policy implications

The integration of sex and gender as precision biomarkers in diagnostic, therapeutic, and prognostic technologies requires ongoing policy development. As machine learning approaches demonstrate the capability to manage complex factors affecting cancer risk or outcome, the regulation of harmonized global data collection, curation, and sharing becomes imperative [60]. These policies must accommodate the ethical and legal considerations highlighted earlier to ensure responsible use of patient information [71].

The implementation of national and international campaigns to promote sex- and gender-literate research addresses a fundamental policy need. While funding agencies like the NIH and the European Commission offer dedicated programs for sex and gender research, policymakers must consider incentives for institutions, businesses, and individual researchers to pursue this area actively [38].

Regulatory frameworks

Regulatory agencies face challenges from personalized treatments and emerging technologies that outperform traditional trials and evidence frameworks. Generating and sharing genomic data introduces concerns about information control, privacy, and ethical use [31]. Regulatory evaluation is hindered by the evolving nature of AI/ML algorithms, rare diseases, and diverse clinical contexts [50].

In the United States, the Food and Drug Administration (FDA) is addressing these challenges. The FDA’s Oncology Center of Excellence, the Center for Devices and Radiological Health, the Center for Drug Evaluation and Research, and the Center for Biologics Evaluation and Research participate in a Medical Device Development Tools Program coupled with an oncology biomarker qualification pipeline for drug development tools that address patient selection and treatment modalities ranging from chemotherapy to immunotherapy [13].

Several promising computer-aided detection systems (CADe) and computer-aided diagnosis (CADx) systems have already been marketed (e.g., cardiovascular disease, diabetic retinopathy, and breast, lung, and colon cancers). In (early) cancer diagnosis, the first deep learning–based CADe and CADe + x (combined detection and diagnosis of the pathology) systems have been approved—for instance, by the FDA, China’s National Medical Products Administration, Japan’s Pharmaceuticals and Medical Devices Agency, European Conformity Mark, and/or Singapore’s Health Sciences Authority—and approved for screening lung cancer, prostate cancer, colorectal polyp, and breast cancer, respectively [50].

Funding and resources

As technology has advanced over the past decade, academic research groups have been faced with challenges in managing their dedicated instrument portfolios. Keeping scientific equipment operational and compliant with the highest legal regulations often requires more effort than can be realistically expected from research staff, risking impaired operational efficiency and instrument readouts [33]. Addressing this issue, several companies have emerged, dedicated to managing the complete instrument infrastructure on behalf of academic institutions, ensuring compliance with quality standards and legal frameworks.

Navigating matters of compliance, technical competency, statutory calibration, and the impact of the General Data Protection Regulation (GDPR), which came into effect in Europe during May 2018, can be complex [64]. Awareness of these developments is vital even for laboratories with a limited range of instrumentation. Thankfully, a partnership between the Wellcome Trust, the Government Chemist, and the Laboratory Equipment Expert has facilitated the provision of a quality decision process intended to assist in instrument management under these regulations [51, 52].

Reflecting on engagement with grant funders such as the Wellcome Trust, it is evident that their scope has expanded over the years. Beyond financial support, the Wellcome Trust and allied organizations now play a pivotal role in endorsing equipment procurement strategies, academic recruiting, laboratory efficiency, and space occupancy [52]. As researchers invest significant time and resources in producing academic data, outlining intentions for storage, security, or academic credibility becomes imperative. Scientists can leverage this engagement to influence infrastructure development, thereby mitigating risks associated with overexertion in data management.

Education and training

Education and training are paramount in integrating sex and gender analyses into biomedical research. Incorporating these frameworks in university curricula sensitizes future researchers to their importance, underscoring regulatory expectations [24]. Some cancer policy fellowships offer grants focused on these topics, and comprehensive resources like the Lancet series on sex and gender-based medicine and the NIH’s Educational Portal facilitate self-directed learning. Despite their importance, formal training in sex and gender is scarce, often confined to isolated university lectures. Enhancing educational offerings and documenting available materials constitute vital steps to develop and motivate a research workforce equipped to detect and pursue sex- and gender-specific biomarkers [43].

Curriculum development

Recognition of sex and gender differences is gaining momentum in basic and clinical research. Progress in establishing inclusion of sex and gender in research has been slow for several reasons, including a lack of training and failure to appreciate the importance of the task [43]. For example, although about half of all European medical schools include some components of sex- and gender-based medicine (SGBM) in their curriculum, the majority devote less than 5 h to the topic, and only a quarter of surveyed deans believe their students are being adequately prepared [43]. Including SGBM in health professions curricula enhances students’ sex- and gender-based knowledge and consistently produces positive shifts in attitudes and skills. To meet the need for education in SGBM, a multidisciplinary team undertook a curriculum development project. This project developed a comprehensive set of SGBM student learning objectives, curated peer-reviewed evidence-based teaching materials, and created case studies, all of which are freely available to faculty at med.stanford.edu/sexgendermedicine.

Common content areas to foster interprofessional collaborations when addressing sex and gender differences should meet the general training needs of professional education, including physician–patient communications, biological considerations, selected conditions, behavioral health, and wellness and prevention. These categories are dynamic, change across the lifespan, and are influenced by biological (sex) and sociological (gender) components. Gathering evidence on the added value of including training in sex and gender medicine as routine health professional education is crucial to initiate and sustain change. Interprofessional collaborations are necessary to identify current resources and develop new ones. It is erroneous to assume that pathways or mechanisms identified from experiments using material from one sex automatically apply to the other without corroboration. Hormonal, cultural, or environmental factors may influence the behavior of cells and tissues in isolation. Most animal studies focus on sex differences, except those specifically evaluating environmental or social interactions. The constructs of sex and gender influence all aspects of innovation in science, mathematics, engineering, and medicine.

Interdisciplinary collaboration

Interdisciplinary research programs require inputs from multiple fields of study, frameworks to integrate diverse information and knowledge, and research modes supporting interdisciplinary collaborations [48]. Interdisciplinary teams commonly face resistance to change and a lack of accountability for boundary-spanning efforts [12]. Because these challenges naturally arise in individual, disciplinary, and institutional domains, successful interdisciplinary teams develop strategies to overcome such impediments [27]. Supportive environments that foster the growth of research collaborative networks, encouraging an interdisciplinary ethos, are critical for advancing promising ideas and research [20]. Toward this goal, several merging trends encourage a collaborative climate and produce a fertile scientific environment. First, naturally interdisciplinary domains have come to the scientific fore, including areas that enable the convergence of formerly isolated disciplines into a connected network. In the natural and physical sciences, environment and sustainability, human health and well-being, and security have captured the academic agenda [48]. Such grand challenges, whenever adequately framed, appeal to scientists from widely varied disciplinary backgrounds and, when properly supported, serve as a focus for science-policy-societal deliberations. In the medical and health sciences, the challenge of translating basic research into the clinical and applied arena has also stimulated collaborative and interdisciplinary exploratory endeavors. Institutions adopt interdisciplinary research institutional structures to facilitate this transition, including interdisciplinary and translational research centers, and the organization of research around programs and projects rather than departments and divisions [20].

Conclusion

Sex and gender considerations have historically been neglected in cancer diagnosis and prognosis [37, 74]. As sexual dimorphism is associated with substantially different incidence and mortality rates for most human cancer types, this is an important oversight [24, 72]. To achieve truly individualized, precision medicine, sex- and gender-specific approaches must be developed and integrated into standard practice [36, 53]. Large-scale, system-wide studies that incorporate sex and gender as precision medicine implementation variables are needed to characterize the full extent of sexual dimorphism in cancer incidence, prognosis, and mortality and to determine which cancer types can benefit most from such approaches [37, 51]. Artificial intelligence (AI)-driven algorithms hold great potential to complement expert clinicians and researchers in rapidly identifying individual cancer cases that are more likely to benefit from sex- or gender-specific precision diagnostics and treatments, but this requires research that is socially and scientifically reproducible and reflective of the populations it intends to serve [53, 74].

Author contributions

Arun Karnwal: conceptualisation, writing—original draft, formal analysis, methodology, data curation; Aqueel-Ur-Rehman: writing—review and editing, methodology, validation, supervision, formal analysis; Gaurav Kumar and Amar Yasser Jassim: writing—review and editing, validation, formal analysis; Natalia Nesterova: writing—review and editing, validation, formal analysis; Abdel Rahman Mohammad Said Al-Tawaha: writing—review and editing, methodology, validation, formal analysis

Funding

No funds or grants were received for this study.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing of interests

There is no conflict of interest between the authors regarding this paper.