Abstract
Background: Artificial intelligence (AI) algorithms can be applied in breast cancer risk prediction and prevention by using patient history, scans, imaging information, and analysis of specific genes for cancer classification to reduce overdiagnosis and overtreatment. This scoping review aimed to identify the barriers encountered in applying innovative AI techniques and models in developing breast cancer risk prediction scores and promoting screening behaviors among adult females. Findings may inform and guide future global recommendations for AI application in breast cancer prevention and care for female populations. Methods: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O’Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. Results: In the field of breast cancer risk detection and prevention, the following AI techniques and models have been applied: Machine and Deep Learning Model (ML-DL model) (n = 1), Academic Algorithms (n = 2), Breast Cancer Surveillance Consortium (BCSC), Clinical 5-Year Risk Prediction Model (n = 2), deep-learning computer vision AI algorithms (n = 2), AI-based thermal imaging solution (Thermalytix) (n = 1), RealRisks (n = 2), Breast Cancer Risk NAVIgation (n = 1), MammoRisk (ML-Based Tool) (n = 1), Various MLModels (n = 1), and various machine/deep learning, decision aids, and commercial algorithms (n = 7). In the 11 included studies, a total of 39 barriers to AI applications in breast cancer risk prediction and screening efforts were identified. The most common barriers in the application of innovative AI tools for breast cancer prediction and improved screening rates included lack of external validity and limited generalizability (n = 6), as AI was used in studies with either a small sample size or datasets with missing data. Many studies (n = 5) also encountered selection bias due to exclusion of certain populations based on characteristics such as race/ethnicity, family history, or past medical history. Several recommendations for future research should be considered. AI models need to include a broader spectrum and more complete predictive variables for risk assessment. Investigating long-term outcomes with improved follow-up periods is critical to assess the impacts of AI on clinical decisions beyond just the immediate outcomes. Utilizing AI to improve communication strategies at both a local and organizational level can assist in informed decision-making and compliance, especially in populations with limited literacy levels. Conclusions: The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy.
Keywords: artificial intelligence, machine learning, breast cancer screening, risk prediction, women
1. Background
Breast cancer incidence rates among women have slowly increased per year by 0.5% [1,2]. It is the most diagnosed cancer worldwide, surpassing even lung cancer, accounting for 31% of estimated newly diagnosed cancer cases and 15% of estimated deaths [2,3]. In 2020, breast cancer accounted for an estimated 2.3 million cases and 685,000 deaths [3]. Mainly, breast cancer has posed significant global health challenges, with notable disparities in survival rates among socioeconomically disadvantaged women [4,5]. The incidence rates vary widely among countries, with developed nations like the UK and the USA witnessing high rates due in part to an increased prevalence of risk factors and “more extensive use of mammography screening since the 1980s” [3]. Disparities in health outcomes further complicate the global burden of cancer. For instance, non-Hispanic Black women have a higher mortality rate regarding breast cancer compared to their non-Hispanic White counterparts, and this effect might be more pronounced due to specific social determinants of health such as race, socioeconomic status, and healthcare access [6,7]. The situation is exacerbated in developing countries, where globalization and economic growth are predicted to significantly increase breast cancer incidence by 2040 [8]. In India, urban areas report the highest incidence in the 40–49 age group, contrasting with rural areas where the peak is between 65 and 69 years [8].
Screening remains a pivotal strategy in early cancer detection [3]. The WHO and the American Cancer Society have set guidelines for mammography-based screenings, emphasizing their importance for women in specific age groups [8,9,10]. The US Preventive Services Task Forces (USPSTF) recommends that women between 50 and 74 years old receive a mammogram every two years, while women between 40 and 49 years old should make an individualized decision [11,12]. Although breast cancer screening aims at early detection, intrinsic limitations do exist, such as false-positive detections leading to overdiagnosis, unnecessary costs, and negative mental and health well-being [13,14]. The recent COVID-19 pandemic has further strained global cancer care and contributed to disruptions leading to potential delays in breast cancer detection, with countries such as Canada projecting significant increases in advanced-stage diagnoses and related deaths due to screening pauses [10]. As the world grapples with these challenges, the primary goal remains clear: improving cancer screening behaviors through evidence-based strategies to reduce the global cancer burden [9]. One of these strategies is the application of innovative artificial intelligence (AI) models and techniques to predict factors that contribute to informed decision-making about breast cancer screening in at-risk women [15].
AI applications within society are highly prevalent and are beginning to grow substantially within the healthcare field [15,16]. Specifically, radiology and pathology specialties are witnessing the introduction of digital workflows and AI, which offer promising prospects in the field of precision medicine. AI is a broad term that illustrates the concept of “mimicking human intelligence using computers” [17]. Computer programmers create an algorithm, and eventually, the computers can use specific data provided by programmers to make decisions [18]. AI systems and techniques have rapidly evolved over the last 20 years, transitioning from machine learning (ML) to deep learning (DL), to the inclusion of advanced pathways for imaging analysis by allowing healthcare providers to analyze spatial and contextual information from images through multiple layers and convolutional operations. When it comes to daily application of AI systems, radiologists are more effectively managing workflows and detecting suspicious lesions more accurately. Hence, certain AI systems are exceeding human capabilities in predicting long-term breast cancer risk through the development of risk scores tailored for early detection of the disease and adequate intervention.
More exposure to new information improves the ability to interpret data and make decisions [18]. Many cancer screening programs, such as breast cancer, focus on a “one size fits all” approach while prone to inter-observer variability, making patient selection and risk stratification challenging [17,18]. In addition, overdiagnosis and false positives, as previously mentioned, are concerns within the cancer screening process, which could lead to unnecessary treatment and harm to patients [19,20]. AI techniques and models can be applied in cancer prevention and management by using patient history, scans, imaging information, and analysis of specific genes for cancer classification to reduce overdiagnosis and overtreatment [21,22]. This approach can help personalize medicine and benefit all patients, providers, and the healthcare system by providing risk assessment, early cancer detection, diagnosis and classification of cancers, treatment response prediction and efficacy, and helping radiologists process a large amount of data quickly [17,18]. Additionally, there is a need to identify and understand the current circumstances of AI’s application on breast cancer screening and prevention among adults, primarily female adults, for more effective cancer care prevention and recommendations for its future use [23].
This scoping review aims to (1) compare the major outcomes from the application of the different AI models in risk score development and screening rates changes; (2) identify the barriers encountered in applying innovative AI models and techniques in promoting breast cancer screening behaviors and predicting the risk of developing breast cancer among adult females; and (3) highlight recommendations for the adoption, adaptation, and practical implementation of such tools for breast cancer risk score development and incorporation in breast cancer screening efforts. Findings from this review can inform and guide future global recommendations for AI application in breast cancer prevention and care for female populations.
2. Methods
The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study [24]. The Arksey and O’Malley methodology was used as a framework to guide this review [25]. The framework methodology consisted of 5 steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; and (5) Collate, summarize, and report the results.
2.1. Step 1: Identify Research Questions
The two research questions for the scoping review were: (1) What are the barriers encountered in the application of innovative AI techniques and models in promoting breast cancer screening and predicting breast cancer risk among adult females worldwide? And (2) what are global future recommendations for AI application in breast cancer prediction and early detection for female populations?
2.2. Step 2: Search for Relevant Studies
Keywords and mesh terms were developed by a research librarian (MK) experienced with scoping review protocols to allow for the replication of the methodology used for future reviews and other studies relevant to the topic at hand (Supplementary File S1). Search terms included AI, ML, clinical decision aid, computational intelligence, machine computer reasoning, breast cancer, breast neoplasm, malignant tumor of the breast, screening, pre-screening, early detection, model prediction, breast cancer risk, and risk score. The Rayyan platform was used to condense all studies generated from searching four electronic databases (PubMed, Embase, Web of Science, and Cochrane Library) [26]. The review of the literature was conducted over a two-month period from September 2023 to November of 2023. Screening of the articles for inclusion was carried out by primary author (LS) and co-authors (DL, SB, KL, EM, NG, JX, RM, GS).
2.2.1. Inclusion Criteria
Included articles were peer-reviewed studies that were published in English between 2013 and 2023 that (1) examined machine learning and artificial intelligence software and models designed to predict breast cancer risk and/or promote breast cancer screening measures in adult women globally, and (2) explored the role of artificial intelligence and/or machine learning in improving breast cancer screening rates and early detection measures in adult women. AI software and models encompassed all AI techniques such as machine learning, deep learning, robotics, data mining, and reasoning that were specifically designed to predict breast cancer risk in adult women based on social determinants of health, genetic and environmental factors, and other components rendering these women at-risk of developing the disease at one point in their life. Studies were also included if these AI models and techniques were used to influence screening behavior to improve breast cancer screening rates and impact of early detection and prevention efforts in the at-risk female population at a global level.
2.2.2. Exclusion Criteria
Studies were excluded if they (1) addressed cancers other than breast cancer, (2) were not focused on AI, the application of an innovative AI model, technique, or methodology, (3) targeted both male and female patients, (4) included female patients under the age of 18, and (5) were not written in English. Finally, studies that were published as abstracts or used a systematic, scoping, or narrative review methodology were excluded.
2.3. Step 3: Selection of Studies Relevant to the Research Questions
Initial article screening, extraction from the relevant databases, and Rayyan page construction were performed by the lead author (LS). Co-authors (DL, SB, KL, EM, NG, JX, RM, GS) conducted a secondary screening of titles and abstracts in pairs (KL and GS; RM and JX; DL and NG; SB and EM). Consensus on disagreements was reached via discussion involving the initial reviewer (LS).
Co-authors (DL, SB, KL, EM, NG, JX, RM, GS) extracted, summarized, and tabulated the data from all relevant studies. Senior author (LS) reviewed all tabulated data to resolve any discrepancies. Summary tables included one evidence table describing study characteristics (Table 1). Table 2 summarized the barriers encountered in the application of innovative AI techniques and models in promoting breast cancer screening and/or predicting breast cancer risk among adult females globally. Table 3 provides future directions and recommendations in building more effective models for increased accuracy in breast cancer risk prediction and early detection of the disease through the promotion of screening behaviors in adult women. Basic qualitative content analysis was carried out to identify similar themes in recommendations for the advancement of AI models and techniques for breast cancer risk prediction and increased effective screening measures across studies.
Table 1.
Article # | Primary Author/Year | Study Design | Country | Sample Size | Study Population | Study Purpose | Type of AI Model/Technique Applied to Breast Cancer Screening and/or Risk Prediction | Major Outcomes |
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1 | Akselrod-Ballin et al., 2019 [27] | Retrospective Cohort Study | Israel | n = 13,234 | Women who underwent at least one mammogram between 2013 and 2017 in one of the five Assuta Medical Centers imaging facilities, and who had health records for at least 1 year before undergoing mammography in Maccabi Health Services | To evaluate the accuracy and efficiency of a combined machine and deep learning approach for early breast cancer detection applied to a link dataset of digital mammography images and detailed electronic health records |
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2 | Arasu et al., 2022 [28] | Retrospective Case-Cohort Study | USA | n = 13,881 | Women who had a bilateral screening mammogram in 2016 at Kaiser Permanente Northern California, without evidence of cancer on final imaging assessment either at the time of screening or after diagnostic work up of positive screening findings | To examine the ability of 5 artificial intelligence (AI)-based computer vision algorithms, most trained to detect visible breast cancer on mammograms, to predict future risk relative to the Breast Cancer Surveillance Consortium clinical risk prediction model (BCSC v2) |
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3 | Arasu et al., 2023 [29] | Retrospective Case-Cohort Study | USA | n = 13,628 | Women who had a bilateral screening mammographic examination in 2016 at Kaiser Permanente NorthernCalifornia that was negative at final imaging assessment, and were followed until 2021 | To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk |
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4 | Chorev et al., 2023 [30] | Retrospective Cohort Study | Israel | n = 13786 (Israel); n = 1695 (US) | Israeli and American women who underwent screening mammography | To assess the utility of a personalized breast cancer (BC) risk model using comprehensive health records |
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5 | Davalagi et al., 2022 [31] | Mixed-Methods Study | India | n = 768 | Women in the reproductive age group from urban slums of central Karnataka, India | To assess the acceptance and explore challenges for an AI-based screening solution for breast health among the urban slum population |
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6 | Hersch et al., 2015 [32] | Randomized Controlled Trial | Australia | n = 879 | Women aged 48–50 years from New South Wales, Australia | To investigate the impact of including information about breast cancer over detection in a decision aid on informed choice in breast screening |
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7 | Kukafka et al., 2015 [33] | Mixed-Methods Study | USA | n = 34 | Multi-ethnic women from Upper Manhattan, predominantly Hispanic, with a high proportion of low numeracy | To evaluate a decision aid, RealRisks, in improving breast cancer risk perception and decision-making in low-numerate women |
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8 | McGuinness et al., 2022 [34] | Pilot Usability Study | USA | n = 6 | EHR data of 6 patient advocates | To evaluate whether the Fast Healthcare Interoperability Resources (FHIR) standard could support automated breast cancer risk calculations in RealRisks and Breast cancer risk NAVIgation (BNAV) as well as presentation of relevant patient medical history to patients and providers to facilitate shared decision-making |
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9 | Portnoi et al., 2019 [35] | Retrospective Cohort Study | USA | n = 1656 | High-risk women who were screened for breast cancer due to risk factors: genetic mutation, chest radiation, family history of breast cancer, or personal history of breast cancer | To develop a deep learning-based model to analyze breast MR and predict 5-year breast cancer risk |
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10 | Saghatchian et al., 2022 [36] | Feasibility Study | France | n = 196 | Women aged 40 or older, primarily of Caucasian origin, undergoing breast cancer risk assessment | To assess the feasibility of personalized screening and prevention recommendations in the general population through breast cancer risk assessment at a dedicated risk clinic |
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11 | Stark et al., 2019 [37] | Retrospective Cohort Study | USA | n = 64,739 | Study population was derived from the PLCO (Prostate, Lung, Colorectal, and Ovarian) cancer screening trial data, focusing on women who self-identified as White, Black, or Hispanic | To predict breast cancer risk using personal health data and machine learning models |
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Table 2.
Article # | Primary Author/Year | Barriers | Common Barriers in AI Application for Breast Cancer Screening and Risk Prediction |
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1 | Akselrod-Ballin et al., 2019 [27] |
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2 | Arasu et al., 2022 [28] |
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3 | Arasu et al., 2023 [29] |
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4 | Chorev et al., 2023 [30] |
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5 | Davalagi et al., 2022 [31] |
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6 | Hersch et al., 2015 [32] |
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7 | Kukafka et al., 2015 [33] |
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8 | McGuiness et al., 2022 [34] |
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9 | Portnoi et al., 2019 [35] |
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10 | Saghatchian et al., 2022 [36] |
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11 | Stark et al., 2019 [37] |
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Table 3.
Article # | Primary Author/Year | Recommendations | Recurrent Themes for Future Directions in AI Application for Breast Cancer Screening and Risk Prediction |
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1 | Akselrod-Ballin et al., 2019 [27] |
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2 | Arasu et al., 2022 [28] |
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3 | Arasu et al., 2023 [29] |
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4 | Chorev et al., 2023 [30] |
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5 | Davalagi et al., 2022 [31] |
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6 | Hersch et al., 2015 [32] |
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7 | Kukafka et al., 2015 [33] |
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8 | McGuiness et al., 2022 [34] |
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9 | Portnoi et al., 2019 [35] |
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10 | Saghatchian et al., 2022 [36] |
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11 | Stark et al., 2019 [37] |
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2.4. Steps 4 and 5: Data Charting, Collation, Summarization, and Reporting of Results
Study characteristics were tabulated for primary author, year of publication, study design, country, sample size, study population, study purpose, type of AI model or technique applied to breast cancer screening/risk prediction, and major outcomes (Table 1). Common limitations and challenges in the application of AI techniques and models were highlighted across the included studies (Table 2). For Table 3, the three phases of qualitative content analysis for the results of primary qualitative research described by Elo and Kyngas (2008) were applied: (i) preparation, (ii) organizing, and (iii) reporting [38]. In the preparation phase, the unit of analysis is selected, which in our case was relevant lessons learned from each of the included studies in the application of AI techniques and models. This is followed by the organizing phase which encompasses data coding, grouping, categorization, and abstraction of lessons learned across studies for theme identification. The final phase, reporting, consists of sharing the results from the analysis process through tabulated categories.
Content analysis allows the description of the phenomenon in a conceptual form. For the purpose of our paper, deductive analysis was carried out since the resulting structure of the qualitative analysis was operationalized based on previous knowledge in the included studies. Additionally, a deductive approach allowed us to compare theme categories at different time periods of the published studies [38]. This methodology has been widely used in the initial assessment of innovative approaches in healthcare studies [38] and aided in the identification of recurrent themes in recommendations for future advancements in the application of AI to prevent and screen for breast cancer.
3. Results
The initial study extraction yielded 5814 results from PubMed (n = 3054), EMBASE (n = 1455), Web of Science (n = 1245), and Cochrane (n = 60). A total of 2730 duplicate studies were excluded (n = 1226 from PubMed, n = 1344 from Embase, n = 103 from Web of Science, and n= 57 from Cochrane). A total of 3084 studies were screened for eligibility by review of their abstracts. A total of 3070 articles were excluded due to focus on breast cancer diagnosis, treatment, malignancy detection, tumors, or breast density rather than on breast cancer risk detection or screening initiation (n = 1954), lack of artificial intelligence application (n = 592), wrong population (n = 373), wrong study design (n = 144), and publication in a language other than English (n = 7). Fourteen studies were initially selected for full text review and were sourced from PubMed (n = 7), EMBASE (n = 3), Web of Science (n = 1), and Cochrane (n = 3). Upon full article review, three studies were excluded due to being published as abstracts without full texts.
A total of eleven studies were retained for full analysis [27,28,29,30,31,32,33,34,35,36,37], including three retrospective case-cohort studies (n = 3), three retrospective cohort studies (n = 3), two mixed-methods studies (n = 2), and one randomized controlled trial (n = 1), pilot usability study (n = 1), and feasibility study (n = 1). All 11 retained studies were published between 2015 and 2023 and had sample sizes ranging from n = 6 to n = 64,739. The study selection and review process are detailed in Figure 1.
In the field of breast cancer risk detection and prevention, the following AI machine learning tools have been applied: Machine and Deep Learning Model (ML-DL model) (n = 1), Academic Algorithms (e.g., Mirai Algorithm, Globally Aware Multiple Instance Classifier Algorithm (n = 2), Breast Cancer Surveillance Consortium (BCSC), Clinical 5-Year Risk Prediction Model (n = 2), deep-learning computer vision AI algorithms (e.g., Mirai, MammoScreen, ProFound AI, Mia, GMIC) (n = 2), AI-based thermal imaging solution (Thermalytix) (n = 1), RealRisks (n = 2), Breast Cancer Risk NAVIgation (n = 1), MammoRisk (ML-Based Tool) (n = 1), Various MLModels (n = 1), and various machine/deep learning, decision aids, and commercial algorithms (e.g., Logistic Regression, Naive Bayes, Decision Tree, Support Vector Machine, Linear Discriminant Analysis, Neural Networks) (n = 7).
3.1. Major Outcomes
The ML-DL model used by Akselrod-Ballin et al. predicted breast malignancy, identified false negative findings from previous mammograms, and outperformed existing clinically based risk models. When combined with clinical risk models, these ML-DL models improved predictive performance. Meanwhile, a variety of academic and commercial algorithms demonstrated significantly higher sensitivity in predicting breast cancer risk compared to traditional radiology assessments and higher discrimination than the BCSC model (n = 2). Additionally, the combined use of AI algorithms and the BCSC clinical risk model marked a significant increase in predictive accuracy (n = 2). This combination also reduced over-screening and under-screening of patients. They also showed improved performance when trained for shorter periods (n = 2). Furthermore, an ML model using clinical data showed improved model performance over time and helped identify novel clinically relevant patterns. The AI-based thermal imaging solution, Thermalytix, was effective in breast cancer screening. Decision aids such as RealRisks improved accuracy in breast cancer risk perception. MammoRisk, an ML-based tool, influenced changes in risk scores and screening decisions based on polygenic risk scores (PRS). The application of various ML models, including logistic regression and neural networks, provided diverse insights contributing to breast cancer risk detection and prevention. In summary, these AI techniques and models have significantly contributed to enhancing the accuracy of breast cancer risk detection and mammography screening, demonstrating the potential for improved early detection and patient-specific screening strategies (Table 1).
3.2. Common Barriers in AI Applications for Breast Cancer Prediction and Prevention
In the 11 included studies, a total of 39 barriers to AI applications in breast cancer prediction and prevention were identified (Table 2). The most common barriers in the application of innovative AI techniques and models to promote breast cancer screening behavior and improve breast cancer risk detection included lack of external validity and limited generalizability (n = 6), as AI was used in studies with either a small sample size or datasets with missing data. Many studies (n = 5) also encountered selection bias due to the exclusion of certain populations based on characteristics such as race/ethnicity, family history, or past medical history. In regard to challenges encountered by healthcare providers in the application of such tools and models, financial limitations (n = 2) and negative attitudes of female patients toward screening services (n = 2) additionally impacted the role of AI in breast cancer screening. The breast cancer risk models commonly demonstrated high levels of uncertainty (n = 3) and could not estimate when women should begin screening (n = 1). They also faced limitations with technical aspects, such as graphic demand (challenges in producing high-quality quantitative image feature analysis-based prediction models) (n = 1), long-term outcome prediction (n = 1), precise localization (n = 1), and differentiation between calcification and mass (n = 1). After receiving the results, patients had only a limited understanding of them, in part due to their complexity (n = 1). Finally, screening risk with underestimation could impact patient future decision making, and decreasing follow up (n = 1) (Table 2).
3.3. Lessons Learned and Future Directions
There are many considerations and future directions for the application of AI techniques and models for breast cancer screening (Table 3). First, AI models urgently need to include a broader spectrum and more complete predictive variables for risk assessment. Hence, there is a need to invest in generating diverse datasets to enhance the practicality and validity of AI models for breast cancer screening. Second, investigating long-term outcomes with improved follow-up periods is critical in assessing the impacts of AI on clinical decisions beyond just the immediate outcomes. Third, to enhance external validity, there are avenues for improvement, such as addressing issues with incomplete variable datasets and small sample sizes that could impact the accuracy of findings, along with including diverse population groups to avoid selection bias and ensure the generalizability of AI models. Fourth, personalized risk assessments and screenings are essential for cancer prevention strategies and can be improved by expanding datasets to include a broader spectrum of predictive variables for risk assessment. Fifth, to increase general applicability, the models should be validated with other populations to address potential biases related to race/ethnicity, family history, or past medical history that might arise in dataset selection. Finally, utilizing AI to improve communication strategies at both a local and organizational level can assist in informed decision-making and compliance, especially in populations with limited literacy levels. A concise set of variables such as benefits, harms, and statistics need to be present and clear to provide the opportunity for informed decision-making on breast cancer screening and improve patient-provider communication on the role of AI models in breast cancer risk prediction [27,28,29,30,31,32,33,34,35,36,37].
4. Discussion
The purpose of this scoping review was to identify the obstacles encountered when applying current innovative AI techniques and models to foster breast cancer screening behaviors and improve breast cancer risk prediction among adult females. As AI techniques and models continuously evolve in nature and scope, they are more equipped to predict breast cancer risk in women accurately [39,40,41]. Romanov et al.’s model received an AUC of 0.747 when predicting cancer-free mammograms from women who went on to develop breast cancer with high predictive power, while the Mirai model maintained its accuracy across seven different populations across five countries [39,40]. By incorporating diverse demographics into AI algorithms, AI offers the opportunity to individualize care and reduce healthcare disparities, such as racial and socioeconomic bias, and allow healthcare to become more equitable [40,42]. Its application in early breast cancer risk detection has shown advantages towards breast cancer screening adherence by encouraging short-term and long-term actions among women [37]. For instance, women who receive high-risk estimates for breast cancer could potentially be motivated to seek a physician early to begin screening and take preventative actions, like hormone therapy replacement or chemoprevention, before breast cancer arises [37]. Some AI models have been designed to also identify those women at high risk for poor psychological resilience after breast cancer diagnosis, to provide early resources to women most in need to improve mental health and quality of life in the future [43]. In addition, AI can serve as a feasible and affordable option, especially in promoting healthcare access in underserved and resource-poor populations [31,37]. At the organizational level, implementing AI models can reduce the burden on the healthcare system, as demonstrated by Ng et al.’s study, which noted a 45% workload reduction while still enhancing breast cancer detection [44]. Overall, the incorporation of AI alongside physicians has been shown to significantly reduce diagnostic time and enhance diagnostic accuracy, ultimately providing efficiency within the workplace [45,46,47].
It is crucial to address the major barriers that limit the worldwide implementation of AI models for improving breast cancer screening rates and early detection through risk scores. One significant barrier identified from this review is the limited generalizability of AI models due to small sample sizes or incomplete variable datasets. These limitations are frequently reported and stem from the challenges associated with gathering large, diverse, and comprehensive datasets that accurately reflect the broader population [48]. The issue underscores the need for additional funding to support the collection of data that accurately represents diverse populations globally, ensuring inclusivity in risk assessment models and their application across larger and more diverse sample sizes [49,50]. Moreover, integrating social determinants of health (SDOH) into developed risk scores is imperative to ensure these tools are more inclusive and accurately represent the populations at risk [51]. SDOH encompasses a range of factors, such as socioeconomic status, education, neighborhood and physical environment, employment, and social support networks, as well as access to healthcare [52]. By incorporating these factors into AI-driven risk assessments, models can provide a more nuanced and comprehensive evaluation of an individual’s risk for developing breast cancer [53]. This approach not only enhances the precision of risk scores but also addresses the disparities in healthcare access and outcomes among different demographic groups, particularly underserved and minority populations, by taking into consideration the underlying factors contributing to increased breast cancer risk in such racial and ethnic groups [51,52,53]. In addition to challenges related to data diversity, model generalizability, and the integration of comprehensive risk factors, this review found that financial limitations and technical challenges hinder the potential of AI and ML tools to revolutionize breast cancer screening and early detection. Overcoming these obstacles requires concerted efforts to secure additional funding for staff training on effective application of AI models, foster collaborative research initiatives, and develop methodologies for integrating SDOH into AI models, thereby ensuring that these innovative tools can benefit a wider range of populations globally and contribute to the reduction of breast cancer morbidity and mortality [51,52,53].
It has been established throughout this review that the incorporation of AI into breast cancer screening is a promising tool for early detection and improved outcomes, but it also highlights the need for a multi-level approach when discussing ML to enhance general applicability and validity across numerous sociodemographic groups. Some literature has shown AI-based models to be accurate among diverse datasets; however, there remains a need for training these models on more robust, diverse datasets [39]. The existing literature trains these models on large datasets, with hundreds to thousands of patients, yet these often come from a single study site and/or community [40,41]. Research around AI and breast cancer screening needs to invest in generating more diverse datasets to elevate these proof-of-concept models by improving their practicality and reducing data bias. With this said, generating a diverse dataset can be challenging. Shams et al. investigated diversity and inclusion within AI research and found that studies point out an under-representation of minority groups in sampling during model training/testing, that there is less attention on equity and justice in AI design and development in general, and there is a general difficulty in measuring diversity within an algorithm [54]. An obvious solution to overcome this is to share data across institutions, but this becomes highly implausible due to patient privacy policies. To circumvent this, researchers could share their models while data remains local to the study site, the concept of federated learning, to further develop their algorithms [55]. Until minority groups are considered in the design, development, and implementation of AI systems, these groups are potentially not receiving any benefit from such technologies [54,55].
Moreover, underserved minority groups often face barriers to informed decision-making due to limited healthcare resources and lower health literacy rates [56,57]. Consequently, lower health literacy rates are associated with numerous poor health and behavioral outcomes [58]. Notably, individuals with inadequate self-reported health literacy have been found less likely to be adherent to mammography guidelines and have been associated with increased cancer fatalism [59,60]. This highlights a clear deficit in how health-related information is presented or communicated [61]. AI can play a pivotal role in developing an improved decision-making aid on breast cancer screening in underserved communities. The release of more widely available AI platforms, such as OpenAI’s ChatGPT and Google’s Bard, has increased public consciousness about AI, with one study finding 80% of Americans willing to use AI-power tools in their health management, underscoring its potential uses in public health across a broad community [62]. Some of these conversational chatbots have already been investigated in cancer screening, prevention, and management with some success [63,64]. Beyond chatbots, AI can be applied in redesigning existing patient education materials to different reading levels for patients [65]. The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy [61,62,63,64,65].
If these concerns are not addressed within the domain of breast cancer screening development, late-stage diagnosis continues to be a considerable burden for patients and their providers. To add to this, breast cancer screening has declined due to the COVID-19 pandemic in the US [23]. Women from racial/ethnic minorities (i.e., American Indian/Alaskan Native, Asian/Pacific Islander, Hispanic) and rural areas particularly are seeing great declines in screening test rates [23]. This recently observed trend may consequently result in delayed identification and late-stage disease diagnosis. AI can potentially step in by improving patient education and risk assessment/stratification, as well as contribute to a more automated, cost-effective approach that would hopefully enhance existing and develop new screening and diagnostic approaches as it relates to breast cancer, benefiting both healthcare providers and at-risk women [19,20,21,22,23].
Limitations
Findings from this review should be interpreted in the context of study limitations. Although a comprehensive search across four databases was carried out for article selection that are relevant to our inclusion criteria, this review did not include tracing of reference lists, manual searches of journals, or grey literature. Additionally, this review only focused on breast cancer screening and risk prediction measures, and excluded breast cancer diagnostic measures. Broader reviews are recommended to account for other sources of literature and extend to diagnostic and management measures rather than focusing solely on preventive measures. Second, artificial intelligence in healthcare is a rapidly evolving field, so it is possible that some studies were not included due to the unintentional omission of search terms. Collaboration with a research librarian for a thorough development of mesh terms to include technical keywords relevant to machine learning and artificial intelligence has likely mitigated this concern. Third, since this is a scoping review, a formal assessment of the quality of the included studies was beyond the scope of this paper. Future systematic reviews should apply a validated checklist from the AI field to adequately assess the application and limitations of the diverse AI tools in predicting breast cancer and promoting screening behavior.
5. Conclusions
This scoping review describes efforts to apply innovative AI techniques and models to improve breast cancer screening rates and enhance the accuracy of breast cancer risk prediction scores. Results may contribute to a broader understanding of the limitations of these tools in breast cancer screening and breast cancer prevention measures, particularly in developing countries with limited affordability and quality of such innovative resources. This study can inform future AI healthcare specialists on more effective ways to improve the global reach and sustainability of these tools in underserved female communities who are at-risk of developing breast cancer.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm13092525/s1.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research received no external funding.
Footnotes
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References
- 1.Vargas-Cardona H.D., Rodriguez-Lopez M., Arrivillaga M., Vergara-Sanchez C., García-Cifuentes J.P., Bermúdez P.C., Jaramillo-Botero A. Artificial intelligence for cervical cancer screening: Scoping review, 2009–2022. Int. J. Gynaecol. Obstet. 2023;165:566–578. doi: 10.1002/ijgo.15179. [DOI] [PubMed] [Google Scholar]
- 2.Siegel R.L., Miller K.D., Wagle N.S., Jemal A. Cancer statistics, 2023. CA Cancer J. Clin. 2023;73:17–48. doi: 10.3322/caac.21763. [DOI] [PubMed] [Google Scholar]
- 3.Lei S., Zheng R., Zhang S., Wang S., Chen R., Sun K., Zeng H., Zhou J., Wei W. Global patterns of breast cancer incidence and mortality: A population-based cancer registry data analysis from 2000 to 2020. Cancer Commun. 2021;41:1183–1194. doi: 10.1002/cac2.12207. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Karanth S., Fowler M.E., Mao X., Wilson L.E., Huang B., Pisu M., Potosky A., Tucker T., Akinyemiju T. Race, socioeconomic status, and health-care access disparities in ovarian cancer treatment and mortality: Systematic review and meta-analysis. JNCI Cancer Spectr. 2019;3:pkz084. doi: 10.1093/jncics/pkz084. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Ginsburg O., Bray F., Coleman M.P., Vanderpuye V., Eniu A., Kotha S.R., Sarker M., Huong T.T., Allemani C., Dvaladze A., et al. The global burden of women’s cancers: A grand challenge in global health. Lancet. 2017;389:847–860. doi: 10.1016/s0140-6736(16)31392-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Collin L.J., Jiang R., Ward K.C., Gogineni K., Subhedar P.D., Sherman M.E., Gaudet M.M., Breitkopf C.R., D’angelo O., Gabram-Mendola S., et al. Racial disparities in breast cancer outcomes in the metropolitan Atlanta area: New insights and approaches for health equity. JNCI Cancer Spectr. 2019;3:pkz053. doi: 10.1093/jncics/pkz053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Newman L.A., Kaljee L.M. Health disparities and triple-negative breast cancer in African American women: A review: A review. JAMA Surg. 2017;152:485–493. doi: 10.1001/jamasurg.2017.0005. [DOI] [PubMed] [Google Scholar]
- 8.Kashyap D., Pal D., Sharma R., Garg V.K., Goel N., Koundal D., Zaguia A., Koundal S., Belay A. Global increase in breast cancer incidence: Risk factors and preventive measures. Biomed Res. Int. 2022;2022:9605439. doi: 10.1155/2022/9605439. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- 9.Harper D.M., Plegue M., Jimbo M., Sheinfeld Gorin S., Sen A. US women screen at low rates for both cervical and colorectal cancers than a single cancer: A cross-sectional population-based observational study. eLife. 2022;11:e76070. doi: 10.7554/elife.76070. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Figueroa J.D., Gray E., Pashayan N., Deandrea S., Karch A., Vale D.B., Elder K., Procopio P., van Ravesteyn N.T., Mutabi M., et al. The impact of the Covid-19 pandemic on breast cancer early detection and screening. Prev. Med. 2021;151:106585. doi: 10.1016/j.ypmed.2021.106585. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Breast Cancer Screening Cancer.gov. [(accessed on 29 February 2024)]; Available online: https://progressreport.cancer.gov/detection/breast_cancer.
- 12.Breast Cancer: Screening. Uspreventiveservicestaskforce.org. Published January 11, 2016. [(accessed on 29 February 2024)]. Available online: https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/breast-cancer-screening.
- 13.Richman I.B., Long J.B., Soulos P.R., Wang S.Y., Gross C.P. Estimating breast cancer overdiagnosis after screening mammography among older women in the United States. Ann. Intern. Med. 2023;176:1172–1180. doi: 10.7326/m23-0133. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Løberg M., Lousdal M.L., Bretthauer M., Kalager M. Benefits and harms of mammography screening. Breast Cancer Res. 2015;17:63. doi: 10.1186/s13058-015-0525-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Koh D.-M., Papanikolaou N., Bick U., Illing R., Kahn C.E., Kalpathi-Cramer J., Matos C., Martí-Bonmatí L., Miles A., Mun S.K., et al. Artificial intelligence and machine learning in cancer imaging. Commun. Med. 2022;2:133. doi: 10.1038/s43856-022-00199-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Davenport T., Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc. J. 2019;6:94–98. doi: 10.7861/futurehosp.6-2-94. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Hunter B., Hindocha S., Lee R.W. The role of artificial intelligence in early cancer diagnosis. Cancers. 2022;14:1524. doi: 10.3390/cancers14061524. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Can Artificial Intelligence Help See Cancer in New Ways? National Cancer Institute. Published March 22, 2022. [(accessed on 29 February 2024)]; Available online: https://www.cancer.gov/news-events/cancer-currents-blog/2022/artificial-intelligence-cancer-imaging.
- 19.Carter S.M. Why does cancer screening persist despite the potential to harm? Sci. Technol. Soc. 2021;26:24–40. doi: 10.1177/0971721820960252. [DOI] [Google Scholar]
- 20.Dembrower K., Crippa A., Colón E., Eklund M., Strand F. Artificial intelligence for breast cancer detection in screening mammography in Sweden: A prospective, population-based, paired-reader, non-inferiority study. Lancet Digit. Health. 2023;5:e703–e711. doi: 10.1016/s2589-7500(23)00153-x. [DOI] [PubMed] [Google Scholar]
- 21.Zhang B., Shi H., Wang H. Machine learning and AI in cancer prognosis, prediction, and treatment selection: A critical approach. J. Multidiscip. Healthc. 2023;16:1779–1791. doi: 10.2147/jmdh.s410301. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Badal K., Lee C.M., Esserman L.J. Guiding principles for the responsible development of artificial intelligence tools for healthcare. Commun. Med. 2023;3:47. doi: 10.1038/s43856-023-00279-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.DeGroff A., Miller J., Sharma K., Sun J., Helsel W., Kammerer W., Rockwell T., Sheu A., Melillo S., Uhd J., et al. COVID-19 impact on screening test volume through the National Breast and Cervical Cancer early detection program, January–June 2020, in the United States. Prev. Med. 2021;151:106559. doi: 10.1016/j.ypmed.2021.106559. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Tricco A.C., Lillie E., Zarin W., O’Brien K.K., Colquhoun H., Levac D., Moher D., Peters M.D.J., Horsley T., Weeks L., et al. PRISMA extension for Scoping Reviews (PRISMA-ScR): Checklist and explanation. Ann. Intern. Med. 2018;169:467–473. doi: 10.7326/M18-0850. [DOI] [PubMed] [Google Scholar]
- 25.Arksey H., O’Malley L. Scoping studies: Towards a methodological framework. Int. J. Soc. Res. Methodol. 2005;8:19–32. doi: 10.1080/1364557032000119616. [DOI] [Google Scholar]
- 26.Ouzzani M., Hammady H., Fedorowicz Z., Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst. Rev. 2016;5:1–10. doi: 10.1186/s13643-016-0384-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Akselrod-Ballin A., Chorev M., Shoshan Y., Spiro A., Hazan A., Melamed R., Barkan E., Herzel E., Naor S., Karavani E., et al. Predicting breast cancer by applying deep learning to linked health records and mammograms. Radiology. 2019;292:331–342. doi: 10.1148/radiol.2019182622. [DOI] [PubMed] [Google Scholar]
- 28.Arasu V.A., Habel L.A., Achacoso N.S., Buist D.S., Cord J.B., Esserman L.J., Hylton N.M., Glymour M.M., Kornak J., Kushi L.H., et al. Comparison of mammography artificial intelligence algorithms for 5-year breast cancer risk prediction. bioRxiv. 2022 doi: 10.1101/2022.01.05.22268746. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Arasu V.A., Habel L.A., Achacoso N.S., Buist D.S.M., Cord J.B., Esserman L.J., Hylton N.M., Glymour M.M., Kornak J., Kushi L.H., et al. Comparison of mammography AI algorithms with a clinical risk model for 5-year breast cancer risk prediction: An observational study. Radiology. 2023;307:e222733. doi: 10.1148/radiol.222733. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Chorev M., Barros V., Spiro A., Evron E., Barkan E., Kagan O., Amit M., Ozery-Flato M., Akselrod-Balin A., Shalev V., et al. Leveraging comprehensive health records for breast cancer risk prediction: A binational assessment. AMIA Annu. Symp. Proc. 2022;2022:385–394. [PMC free article] [PubMed] [Google Scholar]
- 31.Davalagi S.B., Palicheralu B.S., Murthy S.S.N., Hurlihal S. Acceptance of artificial intelligence (AI)-based screening for breast health in urban slums of central Karnataka, India-SWOC analysis. J. Family Med. Prim. Care. 2022;11:6023–6028. doi: 10.4103/jfmpc.jfmpc_143_22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Hersch J., Barratt A., Jansen J., Irwig L., McGeechan K., Jacklyn G., Thornton H., Dhillon H., Houssami N., McCaffery K. Use of a decision aid including information on overdetection to support informed choice about breast cancer screening: A randomised controlled trial. Lancet. 2015;385:1642–1652. doi: 10.1016/S0140-6736(15)60123-4. [DOI] [PubMed] [Google Scholar]
- 33.Kukafka R., Yi H., Xiao T., Thomas P., Aguirre A., Smalletz C., David R., Crew K. Why breast cancer risk by the numbers is not enough: Evaluation of a decision aid in multi-ethnic, low-numerate women. J. Med. Internet Res. 2015;17:e165. doi: 10.2196/jmir.4028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.McGuinness J.E., Zhang T.M., Cooper K., Kelkar A., Dimond J., Lorenzi V., Crew K.D., Kukafka R. Extraction of electronic health record data using Fast Healthcare Interoperability Resources for automated breast cancer risk assessment. AMIA Annu. Symp. Proc. 2021;2021:843–852. [PMC free article] [PubMed] [Google Scholar]
- 35.Portnoi T., Yala A., Schuster T., Barzilay R., Dontchos B., Lamb L., Lehman C. Deep learning model to assess cancer risk on the basis of a breast MR image alone. AJR Am. J. Roentgenol. 2019;213:227–233. doi: 10.2214/AJR.18.20813. [DOI] [PubMed] [Google Scholar]
- 36.Saghatchian M., Abehsera M., Yamgnane A., Geyl C., Gauthier E., Hélin V., Bazire M., Villoing-Gaudé L., Reyes C., Gentien D., et al. Feasibility of personalized screening and prevention recommendations in the general population through breast cancer risk assessment: Results from a dedicated risk clinic. Breast Cancer Res. Treat. 2022;192:375–383. doi: 10.1007/s10549-021-06445-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Stark G.F., Hart G.R., Nartowt B.J., Deng J. Predicting breast cancer risk using personal health data and machine learning models. PLoS ONE. 2019;14:e0226765. doi: 10.1371/journal.pone.0226765. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Elo S., Kyngäs H. The qualitative content analysis process. J. Adv. Nurs. 2008;62:107–115. doi: 10.1111/j.1365-2648.2007.04569.x. [DOI] [PubMed] [Google Scholar]
- 39.Romanov S., Howell S., Harkness E., Bydder M., Evans D.G., Squires S., Fergie M., Astley S. Artificial intelligence for image-based breast cancer risk prediction using attention. Tomography. 2023;9:2103–2115. doi: 10.3390/tomography9060165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Yala A., Mikhael P.G., Strand F., Lin G., Satuluru S., Kim T., Banerjee I., Gichoya J., Trivedi H., Lehman C.D., et al. Multi-institutional validation of a mammography-based breast cancer risk model. J. Clin. Oncol. 2022;40:1732–1740. doi: 10.1200/jco.21.01337. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Balkenende L., Teuwen J., Mann R.M. Application of deep learning in breast cancer imaging. Semin. Nucl. Med. 2022;52:584–596. doi: 10.1053/j.semnuclmed.2022.02.003. [DOI] [PubMed] [Google Scholar]
- 42.Mema E., McGinty G. The role of artificial intelligence in understanding and addressing disparities in breast cancer outcomes. Curr. Breast Cancer Rep. 2020;12:168–174. doi: 10.1007/s12609-020-00368-x. [DOI] [Google Scholar]
- 43.Manikis G.C., Simos N.J., Kourou K., Kondylakis H., Poikonen-Saksela P., Mazzocco K., Pat-Horenczyk R., Sousa B., Oliveira-Maia A.J., Mattson J., et al. Personalized risk analysis to improve the psychological resilience of women undergoing treatment for Breast Cancer: Development of a machine learning–driven clinical decision support tool. J. Med. Internet Res. 2023;25:e43838. doi: 10.2196/43838. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Ng A.Y., Oberije C.J.G., Ambrózay É., Szabó E., Serfőző O., Karpati E., Fox G., Glocker B., Morris E.A., Forrai G., et al. Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer. Nat. Med. 2023;29:3044–3049. doi: 10.1038/s41591-023-02625-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Lei Y.-M., Yin M., Yu M.-H., Yu J., Zeng S.-E., Lv W.-Z., Li J., Ye H.-R., Cui X.-W., Dietrich C.F. Artificial intelligence in medical imaging of the breast. Front. Oncol. 2021;11:600557. doi: 10.3389/fonc.2021.600557. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Adam R., Dell’Aquila K., Hodges L., Maldjian T., Duong T.Q. Deep learning applications to breast cancer detection by magnetic resonance imaging: A literature review. Breast Cancer Res. 2023;25:87. doi: 10.1186/s13058-023-01687-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Uzun Ozsahin D., Ikechukwu Emegano D., Uzun B., Ozsahin I. The systematic review of artificial intelligence applications in breast cancer diagnosis. Diagnostics. 2022;13:45. doi: 10.3390/diagnostics13010045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Marinovich M.L., Wylie E., Lotter W., Pearce A., Carter S.M., Lund H., Waddell A., Kim J.G., Pereira G.F., Lee C.I., et al. Artificial intelligence (AI) to enhance breast cancer screening: Protocol for population-based cohort study of cancer detection. BMJ Open. 2022;12:e054005. doi: 10.1136/bmjopen-2021-054005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Yang J., Dung N.T., Thach P.N., Phong N.T., Phu V.D., Phu K.D., Yen L.M., Xuan Thy D.B., Soltan A.A., Thwaites L., et al. Generalizability assessment of AI models across hospitals: A comparative study in low-middle income and high income countries. bioRxiv. 2023 doi: 10.1101/2023.11.05.23298109. [DOI] [Google Scholar]
- 50.Yang J., Soltan A.A.S., Clifton D.A. Machine learning generalizability across healthcare settings: Insights from multi-site COVID-19 screening. NPJ Digit. Med. 2022;5:69. doi: 10.1038/s41746-022-00614-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Segar M.W., Hall J.L., Jhund P.S., Powell-Wiley T.M., Morris A.A., Kao D., Fonarow G.C., Hernandez R., Ibrahim N.E., Rutan C., et al. Machine learning-based models incorporating social determinants of health vs traditional models for predicting in-hospital mortality in patients with Heart Failure. JAMA Cardiol. 2022;7:844–854. doi: 10.1001/jamacardio.2022.1900. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Social Determinants of Health Health.gov. [(accessed on 29 February 2024)]; Available online: https://health.gov/healthypeople/objectives-and-data/social-determinants-health.
- 53.Carroll N.W., Jones A., Burkard T., Lulias C., Severson K., Posa T. Improving risk stratification using AI and social determinants of health. Am. J. Manag. Care. 2022;28:582–587. doi: 10.37765/ajmc.2022.89261. [DOI] [PubMed] [Google Scholar]
- 54.Shams R.A., Zowghi D., Bano M. AI and the quest for diversity and inclusion: A systematic literature review. AI Ethics. 2023 doi: 10.1007/s43681-023-00362-w. [DOI] [Google Scholar]
- 55.Rieke N., Hancox J., Li W., Milletarì F., Roth H.R., Albarqouni S., Bakas S., Galtier M.N., Landman B.A., Maier-Hein K., et al. The future of digital health with federated learning. NPJ Digit. Med. 2020;3:119. doi: 10.1038/s41746-020-00323-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Caraballo C., Massey D., Mahajan S., Lu Y., Annapureddy A.R., Roy B., Riley C., Murugiah K., Valero-Elizondo J., Onuma O., et al. Racial and ethnic disparities in access to health care among adults in the United States: A 20-year National Health Interview Survey analysis, 1999–2018. bioRxiv. 2020 doi: 10.1101/2020.10.30.20223420. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Levy H., Janke A. Health literacy and access to care. J. Health Commun. 2016;21((Suppl. S1)):43–50. doi: 10.1080/10810730.2015.1131776. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Fan Z.Y., Yang Y., Zhang F. Association between health literacy and mortality: A systematic review and meta-analysis. Arch. Public Health. 2021;79:119. doi: 10.1186/s13690-021-00648-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Morris N.S., Field T.S., Wagner J.L., Cutrona S.L., Roblin D.W., Gaglio B., Williams A.E., Han P.J.K., Costanza M.E., Mazor K.M. The association between health literacy and cancer-related attitudes, behaviors, and knowledge. J. Health Commun. 2013;18((Suppl. S1)):223–241. doi: 10.1080/10810730.2013.825667. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Fernandez D.M., Larson J.L., Zikmund-Fisher B.J. Associations between health literacy and preventive health behaviors among older adults: Findings from the health and retirement study. BMC Public Health. 2016;16:596. doi: 10.1186/s12889-016-3267-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.The Lancet Why is health literacy failing so many? Lancet. 2022;400:1655. doi: 10.1016/s0140-6736(22)02301-7. [DOI] [PubMed] [Google Scholar]
- 62.Esmaeilzadeh P. Use of AI-based tools for healthcare purposes: A survey study from consumers’ perspectives. BMC Med. Inform. Decis. Mak. 2020;20:170. doi: 10.1186/s12911-020-01191-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Görtz M., Baumgärtner K., Schmid T., Muschko M., Woessner P., Gerlach A., Byczkowski M., Sültmann H., Duensing S., Hohenfellner M. An artificial intelligence-based chatbot for prostate cancer education: Design and patient evaluation study. Digit. Health. 2023;9:20552076231173304. doi: 10.1177/20552076231173304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Wang A., Qian Z., Briggs L., Cole A.P., Reis L.O., Trinh Q.D. The use of chatbots in oncological care: A narrative review. Int. J. Gen. Med. 2023;16:1591–1602. doi: 10.2147/IJGM.S408208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Kirchner G.J., Kim R.Y., Weddle J.B., Bible J.E. Can artificial intelligence improve the readability of patient education materials? Clin. Orthop. Relat. Res. 2023;481:2260–2267. doi: 10.1097/corr.0000000000002668. [DOI] [PMC free article] [PubMed] [Google Scholar]
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