Artificial Intelligence (AI) in health care refers to the use of advanced computational algorithms and technologies to analyze complex medical data and assist in clinical decision-making, diagnostics, treatment planning, and patient management. Using machine learning, natural language processing, and neural networks, AI can process large datasets, identify patterns, and make predictions based on data that are often beyond the capacity of human cognition, thereby improving the precision and efficiency of health care delivery. However, as AI becomes more integrated into health care systems, it faces a fundamental challenge when it comes to narrative-based medicine (NBM), a practice that emphasizes the patient’s lived experience as central to understanding illness and delivering care. Despite the challenges, this synergy of AI and NBM can address the complexities of modern health care and ensure that technology and humanism work together to improve primary care outcomes.

This article argues for the harmonization of AI with NBM and examines the ethical concerns of AI’s integration into health care. We contend that by keeping AI systems human-centric, they can complement NBM’s holistic approach, enhancing primary care without undermining its core values.

What is narrative-based medicine (NBM)?

NBM is a clinical approach that emphasizes the importance of understanding patients’ stories and experiences as central to the practice of medicine. Rather than focusing solely on objective clinical data, NBM values the subjective narratives patients share, which can include their emotions, social contexts, cultural background, and personal interpretations of their illnesses. This approach recognizes that illness is not just a biological process but also a personal, social, and emotional experience, and that these aspects are crucial in diagnosing, treating, and managing diseases.

In primary care, where long-term relationships with patients are common, NBM can be particularly effective. Physicians often become witnesses to patients’ life stories over time, allowing them to make more personalized care decisions that align with the patient’s emotional and social contexts. By acknowledging the uniqueness of each patient’s narrative, primary care physicians are better equipped to address not only physical symptoms but also the underlying emotional or psychosocial issues that may affect health outcomes.

NBM has proven to reduce physician burnout, improve patient adherence to treatments, and strengthen doctor-patient relationships. These benefits make NBM an essential practice in primary care, where emotional engagement and holistic understanding are key to providing quality care.

Ethical concerns and challenges of AI in NBM

The integration of AI into NBM offers exciting potential for improving patient care, but it must be approached with caution and a strong ethical framework. A key ethical dilemma may arise if patients feel depersonalized or their unique stories are reduced to mere data points, which could compromise trust. Furthermore, narrative data often reflect cultural, linguistic, and gender-specific nuances. AI may fail to capture these subtleties, leading to unequal health care outcomes for marginalized groups. For instance, a study highlighted that AI models were less effective at detecting depression in social media posts by Black Americans compared to White Americans, underscoring the importance of inclusive training data to improve AI tools for health care applications. Patients may feel alienated if they perceive that decisions are being made by an algorithm rather than a physician who understands their individual story. 

Another critical concern is the issue of transparency in how AI algorithms derive insights or predictions from narrative data. In contrast, NBM emphasizes open communication and shared decision-making, wherein the patient’s voice is an integral part of the diagnostic and treatment process. Thus, for AI to be successfully integrated into a narrative-based care model, these ethical challenges must be carefully addressed.

Synergies between AI and NBM to enhance narrative medicine

Despite the challenges, AI has the potential to complement NBM by supporting physicians in understanding and integrating patient narratives more effectively. Natural Language Processing (NLP) can analyze and extract meaningful information from unstructured clinical data and patient narratives, such as free-text notes, interview transcripts, or electronic health records. By automating time-consuming tasks like summarizing patient histories and identifying key symptoms, NLP allows physicians to allocate more time to active listening and empathy. This capability enables clinicians to gain a comprehensive understanding of the patient’s history and current illness while maintaining a patient-centered approach to care.

Moreover, AI-driven tools can significantly reduce the administrative burden in health care. Tasks such as appointment scheduling, patient monitoring, and even aspects of electronic health record documentation can be automated, freeing up time for physicians to engage more deeply with their patients. By optimizing the more routine elements of health care, AI can allow physicians to save time. This can potentially help ensure that narrative-based care remains central, as these technologies allow practitioners to have more time to listen to patient stories. The time may be ripe for incorporation of AI-driven tools in clinical practice; more than one in four general practitioners in the United Kingdom report using generative AI to assist in their work.

The importance of keeping AI human-centric

The successful integration of AI into NBM requires training for health care providers to use AI systems in a way that complements their narrative competence. Physicians must be able to interpret AI-generated insights while maintaining their role as the primary interface with the patient. AI should augment, rather than replace, the physician’s capacity to engage empathetically with the patient’s narrative. One proposed solution is to view AI as a supportive tool, designed to assist rather than supplant human judgment. Guidelines such as the World Health Organization’s framework emphasize human oversight, transparency, and accountability in AI systems, ensuring that physicians retain understanding of the patient narrative and can make decisions informed by both data and personal knowledge of the patient. Transparency in AI systems is also essential. Physicians and patients alike must understand how AI-derived conclusions are reached, allowing them to make informed decisions together.

The ultimate goal is to create an AI system that enhances the humanistic aspects of medicine, ensuring that the patient’s story remains central to care. AI should serve as a tool that augments the physician’s capacity to listen, interpret, and respond to patient narratives with empathy and insight. By keeping AI human-centric, we can ensure that health care remains personalized, even in an increasingly data-driven world.

AI in health care presents both opportunities and challenges

The integration of AI into health care presents both opportunities and challenges, particularly in relation to narrative-based medicine. While AI offers powerful tools for analyzing data and improving diagnostic accuracy, it must be implemented in ways that complement the humanistic values at the core of NBM. By harmonizing AI with NBM, we can support primary care, ensuring that the patient’s story remains central to diagnosis and treatment. AI should be viewed not as a replacement for the physician but as a partner in delivering empathetic, patient-centered care. The future of health care lies in the synergy between technology and human connection, where AI enhances the ability of physicians to understand and respond to the unique narratives of each patient.

About the author

	Dr. Nadirah Ghenimi is an Assistant Professor and Chair of the Family Medicine Department at UAE University. She is also a consultant in Family Medicine and Bariatric Medicine. Her research focuses on leveraging artificial intelligence to improve patient-centered care, with a particular emphasis on narrative-based medicine. Dr. Ghenimi is actively engaged in projects addressing obesity management, as well as women's and children's health, particularly within the UAE.
	Romona Govender, an Associate Professor of Family Medicine, is recognized among the top 2% of cited scientists globally. Her research spans clinical epidemiology with a PhD focus on suicidal ideation in HIV-positive patients, contributing over 40 peer-reviewed publications on cardiovascular disease, diabetes, and obesity. Through collaborations in machine learning, she advances disease prediction models in obstetrics, including notable work in preterm birth prediction. Dr. Govender’s dedication to evidence-based medicine and innovative patient care makes her a trusted leader and mentor in family medicine.
	Keymanthri Moodley is a Distinguished Professor in the Department of Medicine and Head of the Division of Medical Ethics and Law, Faculty of Health Sciences, Stellenbosch University, South Africa. This WHO Collaborating Centre in Bioethics is one of twelve in the world and the first on the African continent. Keymanthri is also an Adjunct Professor in the Department of Social Medicine, University of North Carolina-Chapel Hill, USA. She is a specialist family physician and bioethicist. To date, she has served as Principal Investigator on 5 NIH grants and has over 130 publications (journals, books, book chapters). She currently co-chairs the WHO Working Group on Clinical Ethics. Her most recent NIH funded research project explores ethical, legal and social issues (ELSI) in Data Science Innovation in Africa. The group is working on the ELSI of big data and Artificial Intelligence in the African context. Keymanthri is a member of the WHO Ethics and AI Expert Group.

*Feature photo obtained with a standard license on Shutterstock.

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