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. 2024 Mar 5;3:100026. doi: 10.1016/j.esmorw.2024.100026

Impact of artificial intelligence in transforming the doctor–cancer patient relationship

P-E Heudel 1,∗,†, H Crochet 2,†, J-Y Blay 1,3,†
PMCID: PMC12836740  PMID: 41647297

Abstract

Background

The integration of Artificial Intelligence (AI) in oncology is a developing field, impacting the doctor-patient relationship and the efficacy of cancer care. While AI’s role in improving clinical efficiency and personalized care through the analysis of vast medical datasets is acknowledged, its full scope and impact are not yet completely understood.

Materials and methods

This article synthesizes empirical studies and expert opinions to offer a comprehensive understanding of AI’s current role in oncology. The methodology focuses on exploring the balance between technological advancements and the essential elements of patient-centered care.

Results

The paper hypothesizes that AI can enhance the quality of cancer care, but notes challenges such as potential depersonalization, data privacy issues, and ethical dilemmas. It also highlights AI’s potential in facilitating Shared Decision-Making, empowering patients and assisting oncologists in making more informed decisions. However, the risk of AI-driven paternalism and the need for balancing AI recommendations with patient autonomy are discussed.

Conclusions

AI holds significant potential to transform cancer care. The paper concludes that for AI to be beneficial, its integration should be collaborative and patient-centered, ensuring that technological advancements support and enhance the quality of the doctor-patient relationship, rather than undermining it. The article emphasizes the importance of transparent communication, patient education about AI, and the need for oncologists to effectively understand and convey AI-generated data.

Key words: artificial intelligence, cancer, doctor–patient relationship, shared decision making

Highlights

  • •

    AI in oncology: enhances diagnosis, personalizes treatment, improves outcomes.

  • •

    AI challenges: balances technology with empathy, trust, and patient care ethics.

  • •

    AI and decision making: influences choices, empowers patients, respects autonomy.

  • •

    Patient-centered AI: prioritizes patient care, supports doctor–patient relationship.

Introduction

The integration of artificial intelligence (AI) into health care, particularly in oncology, marks a paradigm shift in how cancer care is delivered and experienced. AI’s potential to transform cancer diagnosis, treatment, and patient management has been increasingly recognized.1, 2, 3, 4 AI in oncology has evolved from classical machine learning models to advanced technologies, enhancing cancer understanding, diagnosis, and treatment. Early methods like support vector machines and random forests analyzed clinical data to inform treatment, but their effectiveness waned with increasing data complexity.5 Now, advanced AI, including large language models, sifts through extensive medical literature and patient records, aiding in decision making. Convolutional neural networks (CNNs) have revolutionized medical imaging, improving diagnostic accuracy, as demonstrated in Esteva et al.’s 2017 study on skin cancer classification using deep CNNs.6 Vision transformers and image analysis provide precise interpretations of complex visual data, such as histopathology slides or computed tomography scan.7 Additionally, multimodal transformer models integrate genetic, imaging, and clinical data for a comprehensive patient analysis and treatment strategies, representing AI’s cutting-edge potential in oncology.8 For example, in recent years, significant advancements in deep learning have revolutionized the potential for personalized and predictive oncology care. Notably, Courtiol et al. demonstrated the capability of deep learning to improve prediction of mesothelioma patient outcomes.9 Complementing this, Hosny et al. utilized deep learning for lung cancer prognostication, significantly enhancing the precision of treatment outcome predictions and patient survival rates.10 Furthermore, Ogier du Terrail et al. applied deep learning to predict complete histological response to neoadjuvant chemotherapy in triple-negative breast cancer from whole-slide tissue images, providing profound insights for treatment strategies and patient management.11 Together, these studies underscore the transformative impact of AI on diagnosis, treatment response prediction, and prognostication, indicating its increasingly integral role in clinical workflows and its potential to comprehensively reshape the landscape of oncological care. However, the understanding of how AI will influence the doctor–cancer patient relationship remains unclear, even though it is a cornerstone of effective cancer care.12,13

Cancer care is inherently complex and multifaceted, which involves both technical aspects with the need of a timely and accurate diagnosis and clinical practice guidelines-guided treatment decisions often involving long-term treatment and emotional support. The doctor–patient relationship in this context is critical, as it significantly impacts patient outcomes, satisfaction, and compliance with treatment.14 With the advent of AI, the dynamics of this relationship are evolving. AI tools, such as machine learning algorithms and predictive analytics, are now being used to assist and refine cancer diagnosis, predict treatment outcomes, and guide therapy plans.11,15 While these advancements bear the promises of an improved clinical efficiency and patient outcomes, they also introduce challenges. Concerns on the depersonalization of care, data privacy, and ethical implications of AI decision making are increasingly prevalent.16,17 Hence, the integration of AI in cancer care raises questions about the alteration of the traditional roles and responsibilities of health care providers and patients, potentially impacting trust and communication.18,19

We aim to explore these dimensions, drawing on recent empirical studies and expert opinions to provide a comprehensive understanding of the changing landscape of the doctor–cancer patient relationship with the integration of AI in routine care. We hypothesize that while AI can significantly improve cancer diagnoses and guide treatment, its integration must be managed carefully to preserve and enhance the human elements of empathy, trust, and communication that are central to effective patient care.19 In the following sections, we will examine the specific ways in which AI is influencing cancer diagnosis and treatment, delve into the challenges and ethical considerations it raises, and discuss strategies to effectively integrate AI into cancer care without diminishing the quality of the doctor–patient relationship.

Artificial Intelligence and shared decision making in cancer care

The integration of AI in cancer care is not just a technological advancement but a paradigm shift in the dynamics of the doctor–patient relationship, especially within the framework of shared decision making (SDM).20,21 In oncology, where individual patient profiles strongly influence patient outcome, the precision and personalization of care are crucial. AI, as an integral component of clinical decision support systems (CDSS), will play a pivotal role in this arena and may thus define a new triadic AI–doctor–patient relationship22,23 (Figure 1). Cancer treatment involves navigating a complex array of diagnostic information, treatment options, and prognostic uncertainties. AI systems, equipped with advanced algorithms, can analyze greater datasets—encompassing medical imaging, genetic information, and patient health records—to identify patterns and correlations that may elude human analysis.

Figure 1.

Figure 1

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Triadic communication model in oncology with artificial intelligence (AI) mediation.

This capability enables oncologists to make more informed decisions about the most effective and personalized treatment strategies. For instance, AI can assist in identifying which patients are more likely to respond to certain chemotherapies or targeted therapies based on their data characteristics.24,25 However, beyond mere data analysis and predictive capabilities, the incorporation of AI in cancer care also influences the SDM process by providing both patients and doctors with a more comprehensive and nuanced understanding of the disease and potential treatment pathways. It increases both precision and the understanding of the presence of uncertainties.

For cancer patients, who often face emotionally and physically taxing treatment journeys, AI-augmented SDM can provide a sense of empowerment and control over their treatment decisions. This is particularly important in cancer care, where treatment decisions can have profound implications on a patient’s quality of life and overall prognosis. AI-driven tools in CDSS can present oncologists with a range of possible interventions, each with detailed information on efficacy, risks, and compatibility with the patient’s unique health profile. This information aids in crafting a more transparent and informative discussion with patients, enabling a better collaborative decision-making process. From a patient perspective, it enables to more accurately measure the benefits and risks of different treatment options in the context of their personal health goals and lifestyle considerations. On the contrary, this very technical approach may also influence the autonomy of the doctor and holds the risk to generate a paternalistic approach where the patient’s opinion is little or not listened to.26 Although the involvement of mathematical and statistical models in medicine and health care is not new per se, the fact that these models are elaborated through machine learning and AI techniques may sharpen the difficulties. While claiming that AI algorithms are inscrutable may prove exaggerated,27 their functioning and details of performance (specificity, sensibility, etc.) remain puzzling in routine clinical practice for professionals. A proper and accurate description to the patients of the algorithmic logic that underlies diagnosis and therapeutic strategies they receive may prove challenging in this context. Another challenge is the definition of the level of information that patients need and want to acquire about these digital and AI tools.

As always patients do not know the details of all scientific and technological processes involved in medical decisions and treatments proposed by doctors. They only need the relevant information. In this specific context of AI and machine learning, it is important to define ‘relevant information’. Does it mean the same for all patients or depend upon the particularities of each individual? With AI providing recommendations based on data, the decision-making process in health care might shift. Patients might be more inclined to trust and follow AI-driven recommendations, potentially altering the traditional dynamic where the doctor’s expertise is the primary guiding factor.28

The integration of AI in cancer care, particularly through CDSS, is redefining how decisions are made in oncology. It enhances the precision and personalization of cancer treatments, contributing to more informed and collaborative SDM processes. This evolution in care not only has the potential to improve clinical outcomes but also deeply respects and empowers patients in their treatment journey, marking a significant stride in patient-centered oncology.

Cancer care and the challenge of integrating AI

Cancer treatment is inherently complex and multifaceted, necessitating decisions that are both highly personalized and critically impactful.29 Each patient’s cancer journey is unique, influenced by multiple factors including genetics, lifestyle, and co-existing medical conditions. The integration of AI into cancer care has the potential to revolutionize this process. By leveraging machine learning and big data analytics, AI can provide oncologists with enhanced diagnostic tools, more accurate predictions of treatment outcomes, and tailored treatment pathways that are specifically aligned with individual patient profiles. AI tools will serve the deployment of precision medicine of cancer, in situations where outcome is influenced by multiplicity of parameters. For instance, AI can analyze complex genetic information to identify mutations specific to a patient’s cancer, suggesting targeted therapies that are likely to be more effective and have fewer side-effects, helping oncologists in choosing the most suitable approach for each patient.30

However, the integration of AI in cancer care is not without its challenges.31 One of the primary concerns is maintaining a balance between the benefits of advanced technology and the fundamental principles of patient autonomy and human-centered care. This is compounded by the need for extensive, high-quality data to train these models, which can be challenging to obtain due to privacy concerns and the need for standardized data collection protocols.32 Another significant hurdle is the integration of AI tools into existing health care systems and workflows, which often requires substantial changes in infrastructure and processes, as well as training for health care professionals.33 There is also the critical issue of trust and acceptance, both from health care providers who may be skeptical of AI’s reliability and from patients who might have concerns about the impersonality of AI and data privacy.34 Additionally, AI algorithms can suffer from biases if the training data are not representative, leading to potential disparities in care. It thus seems fundamental that oncologists should be able to precisely and easily know the quality level of the training data of the model as well as the scope of these data in order to have a certain confidence in the model’s results and, most importantly, to avoid using the model in a clinical situation that was not included in its training.35,36 A rigorous, independent, national or international evaluation process for AI tools will be mandatory as for all treatment. A robust quality control system is vital for AI tools in health care to ensure their effectiveness and ethical application. As health care decisions are critical, AI must be reliable, accurate, and unbiased so as not to harm its interests.37 To combat bias and ensure equitable outcomes, the system should include diverse, high-quality training data, continuous monitoring for accuracy and biases, transparent reporting, independent auditing, regulatory compliance, and thorough user training. Addressing the nuances of bias is essential for maintaining the integrity and fairness of AI systems. The necessity of such a system is detailed in the article by Char et al., which emphasizes transparency and ongoing monitoring in AI implementation in health care.38 Moreover, the integration of AI into clinical settings highlights the potential mismatch between sophisticated AI recommendations and the available resources, particularly in settings with limited capacity. AI might propose advanced treatments or diagnostics that surpass a facility’s capabilities, creating a ‘technology-resource gap’. A context-aware deployment of AI is crucial, aiming to complement existing health care infrastructure and support professionals, rather than overextending resources. As Parikh et al. emphasize, aligning AI recommendations with practical capabilities is essential.39 Collaboration among AI developers, health care providers, and policymakers is vital to ensure AI tools are both advanced and adapted to real-world contexts, enhancing health care delivery while addressing resource constraints.40 The global spread of AI in health care presents a paradox: the potential to standardize care and diminish disparities, yet also the risk of worsening existing health inequities. If AI remains predominantly accessible in well-resourced areas, it may deepen divides across different socioeconomic groups. Addressing this requires involving diverse stakeholders in AI’s development, investing in universal health care infrastructure and education, and establishing ethical access guidelines. The impact of AI will hinge on strategies and policies aimed at broadening its benefits and reducing health care disparities, as highlighted by Obermeyer et al., who examined AI’s potential to either homogenize care or widen gaps between resource-rich and -poor settings.41

While AI can provide valuable insights, the final treatment decisions should and will remain the result of a collaborative process that respects the patient’s values, concerns, and preferences. Ensuring that patients are not only informed about but also understand the implications of AI-driven recommendations will be essential. This requires transparent communication and an empathetic approach to care, where the technological aspects do not overshadow the human elements of compassion, understanding, and respect for patient choices. There will be a need for oncologists and health care professionals to be adequately trained to interpret and convey AI-generated data effectively.42 They must be skilled not only in the technical aspects of AI tools but also in addressing the ethical and emotional aspects of their use in patient care. The challenge lies in harmoniously blending AI’s capabilities with the nuances of human interaction and ethical medical practice to enhance patient-centered cancer care.

Navigating the intricacies of AI in patient-centered oncology care

The integration of AI into cancer clinical practice introduces complex challenges, particularly in balancing technological advancements with patient autonomy and the doctor–patient communication dynamic.43,44 AI’s ability to enhance decision making with data-driven insights is undeniable, yet it harbors the risk of overshadowing individual patient values and preferences, potentially leading to a new form of paternalism in health care. Here, decisions might lean heavily on AI recommendations rather than a balanced consideration of the patient’s wishes and quality-of-life concerns. To mitigate this, oncologists must emphasize patient-centered communication, ensuring AI’s role remains that of a support tool rather than a decision maker.45 This entails translating AI’s complex data and predictions into understandable information for patients, fostering an environment where they can confidently express their preferences, concerns, and values. Effective communication in this context extends beyond information sharing to engaging in empathetic dialogues that consider patients’ fears, hopes, and personal circumstances in treatment decisions, thus upholding the principle of patient autonomy.46

In this context, patient education about AI’s potential and limitations is crucial. Discussions should cover how AI tools are developed, the kind of data they use, and their recommendation processes. Such transparency can demystify AI in health care and make patients more comfortable with its role in their treatment planning. With the growing use of health AI applications, patients may increasingly consult AI tools for an initial assessment of their symptoms before visiting a doctor. This shift could change the consultation dynamics, with doctors spending more time verifying or refuting AI-provided diagnoses.22 While AI can guide and educate patients, it is crucial to remember that it is not infallible and requires a doctor’s critical evaluation for accurate, comprehensive medical care. This evolution in health care necessitates enhanced communication skills from doctors and careful consideration of ethical and responsibility aspects. The reactions of oncologists to AI vary widely, from skepticism about losing autonomy and skills to overreliance leading to automation bias and reduced diagnostic acumen. For new generations of oncologists, AI offers educational opportunities but also risks eroding patient-related skills. For example, Blease et al. explore clinicians’ attitudes toward AI and the potential for deskilling47,48 discussing the impact of AI on the skills of future doctors, improving their abilities with advanced tools or leading to a decline in crucial skills. With a focus on a balanced approach to AI integration, we highlight the need for ongoing training and a patient-centered focus to ensure AI acts as a complement to, rather than a replacement for, the oncologist’s expertise, thereby enhancing patient care while preserving essential human elements in health care. On the technical side, ensuring the transparency and explainability of AI systems is paramount. Oncologists need a clear understanding of how these tools work, their limitations, and potential biases.49,50 This understanding is vital for maintaining trust and ensuring that AI supports, rather than dictates, treatment decisions. Comprehensive training for oncologists should include the technical, ethical, and communication aspects of AI use. Additionally, the design of AI systems in oncology must be patient-centric, considering patient preferences, values, and lifestyle factors. Patient feedback loops are also essential in aligning AI tools with patient needs, making them more responsive to real-world contexts.51 By focusing on these strategies, the integration of AI in cancer care can be harmonized with patient-centered care, enhancing oncologists’ capacity to deliver personalized care and empowering patients as active, informed participants in their treatment decisions.

Conclusion: A collaborative approach to AI in cancer care

The future trajectory of cancer care is shaping up to be inherently collaborative, where the sophisticated capabilities of AI are seamlessly integrated with the clinical acumen of oncologists. This synergy is poised to transform cancer treatment, making it more data-driven, precise, and tailored to the individual needs of each patient. By keeping patient autonomy and effective communication at the forefront, AI emerges not just as a technological tool but as a catalyst in empowering patients to make well-informed decisions regarding their treatment options. Such a collaborative model can increase SDM, but a rigorous evaluation must be carried out in order to confirm all the expected benefits.

Acknowledgments

Funding

None declared.

Disclosure

The authors have declared no conflicts of interest.

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