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Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Open accessAbstract
We need to consider the ethical challenges inherent in implementing machine learning in health care if its benefits are to be realized. Some of these challenges are straightforward, whereas others have less obvious risks but raise broader ethical concerns.
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“how principles of algorithmic fairness might apply in clinical decision-making has received little attention in the medical literature7,8.”
From Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities (2020) · cites this paper as [8]
In the citing paper
“As AI becomes more pervasive in both public and personal health across diverse populations, there have been increasing concerns, and related examples, of AI solutions leading to inadvertent bias of modeling results (45–48).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [45]
In the citing paper
“Abramoff et al. proposed ethical frameworks for AI27–31 to help proactively address the issue of undesirable algorithmic bias as well as other concerns with AI.”
From Considerations for addressing bias in artificial intelligence for health equity (2023) · cites this paper as [29]
In the citing paper
“These ethical and other concerns with AI in healthcare have been shown by a number of research studies27–29.”
From Considerations for addressing bias in artificial intelligence for health equity (2023) · cites this paper as [29]
In the citing paper
“Trust between public and healthcare systems is essential for effective healthcare delivery [36].”
From Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020) · cites this paper as [36]
In the citing paper
“Ethical concerns about the use of AI in health care include undermining patient data privacy protections and exacerbating the existing tension between providing care and generating profit, as well as introducing a third party into the patient-doctor relationship, which changes expectations of confidentiality and responsibility (10).”
From Artificial intelligence for global health (2019) · cites this paper as [10]
In the citing paper
“Healthcare data are sensitive information about a person [23, 83] which is also related to personal privacy.”
From Ethics and governance of trustworthy medical artificial intelligence (2023) · cites this paper as [23]
In the citing paper
“However, some technical understanding is imperative for quality and safety assessment and therefore create trust in the AI application’s reliability [8, 21, 30].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [8]
In the citing paper
“AI is expected to cause large changes in clinical work practices and requires complementary skills from radiologists [5, 7, 8].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [8]
In the citing paper
“Similarly, the use of AI‐based solutions may raise several moral questions in medicine 64 , 65 : would we trust computers to screen for disease, prioritize treatment, diagnose, treat, discharge?”
From Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology (2020) · cites this paper as [64]
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“Importantly though, ML approaches must continue to be rigorously evaluated for accuracy, risk of bias in predictions, and clinical interpretability of results (Char et al., 2018; Grollemund et al., 2019; Yoon et al., 2022).”
BackgroundDentistry in the Era of Artificial Intelligence: Medical Behavior and Clinical Responsibility
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Deep Learning Enables Fast and Accurate Imputation of Gene Expression
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“Future algorithmic developments therefore hold promise for more effective detection, diagnosis, and treatment (Hosny and Aerts, 2019) and for improved implementation in clinical medicine (Char et al., 2018).”
BackgroundInteligencia artificial en dermatología: ¿amenaza u oportunidad?
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EMM-LC Fusion: Enhanced Multimodal Fusion for Lung Cancer Classification
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“In the health care system, patient’s precaution and patient care are the major goals [3, 4].”
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AI-Driven Medical Imaging Platform: Advancements in Image Analysis and Healthcare Diagnosis
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“Ensuring the robustness and interpretability of AI models is a priority [27, 28, 29], along with addressing ethical considerations and data privacy concerns [30, 31, 32].”
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InfluentialWhat affects consumer behavior in mobile health professional diagnosis applications
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“Ethical Considerations: The use of AI in clinical decision-making raises ethical questions about accountability, consent, and the potential for AI to exacerbate existing healthcare disparities [11].”
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“The ethics behind the intent of machine learning systems also need to be considered [88].”
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“5th intervention-Identification of the Most Visited Hospital Services During Lung Cancer Followed-Up: General Emergencies have been identified as the most visited medical service once a patient is under follow-up by the Oncology Department.”
BackgroundMethodInfluentialAdvantages, Challenges, and Risks of Artificial Intelligence for Radiologists
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The digital revolution and the labour economics of automation: A review
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“Similar machine learning algorithms are increasingly used in finance to assess investment risks and compliance with regulations (Aziz & Dowling, 2019), in recruitment to assess job applicants (Garg et al., 2021) and in health care to aid in medical decision making (Char et al., 2018).”
The “Ecosystem as a Service (EaaS)” approach to advance clinical artificial intelligence (cAI)
Ishii-Rousseau, J. E., Seino, S., Ebner, D. K., Vareth, M., Po, M. J., & Celi, L. A. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000011 · Cited by 14
“This not only marginalizes hospital systems that lack the resources for full-scale cAI development and procurement, but the product purchased may be unusable, with current algorithms demonstrating a lack of transparency behind their creation, and particularly with regard to any biases in the data used [10,11].”
BackgroundIntroduction To Machine Learning
Chopra, D., & Khurana, R. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815124422123010004 · Cited by 14
ESR: Ethics and Society Review of Artificial Intelligence ResearchPreprint
Bernstein, M. S., Levi, M., Magnus, D. C., Rajala, B., Satz, D. M., & Waeiss, Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.11521 · Cited by 25
“These principles share some similarity or even outright inspiration from medical ethics [57] and are even considered within medical contexts [19]; however, unlike the medical profession, computing has no legal and professional accountability mechanisms, nor formal fiduciary relationship [57].”
BackgroundSelf-citationDevelopment of a practical training method for a healthcare artificial intelligence (AI) chatbot
Lin, J., Joseph, T., Parga-Belinkie, J. J., Mandel, A., Schumacher, R., Neumann, K., Scalise, L., Gaulton, J., Christ, L., Leitner, K., et al. (2021) · BMJ Innovations · doi:10.1136/bmjinnov-2020-000530 · Cited by 13
Al-Qudimat, A. R., Fares, Z. E., Elaarag, M., Osman, M., Al‐Zoubi, R. M., & Aboumarzouk, O. M. (2025) · Health Science Reports · doi:10.1002/hsr2.70200 · Cited by 17
“Others, like concerns about data privacy, may pose less evident dangers but raise more general ethical questions [11].”
Cao, C., Zhao, W., Guo, J., Wang, Z., Lü, A., & Liang, C. C. (2025) · The Innovation Medicine · doi:10.59717/j.xinn-med.2025.100154 · Cited by 14
Human-centered AI in healthcare: empowering patients and support persons in clinical decision-making
Sassi, Z., Eickmann, S., Roller, R., Osmanodja, B., Burchardt, A., Tretter, M., Samhammer, D., Dabrock, P., Möller, S., Budde, K., et al. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03298-9 · Cited by 21
“High-quality healthcare includes shared decision-making as a collaborative process that integrates medical expertise with patients’ needs and values [4].”
Artificial Intelligence in Pediatric Nephrology—A Call for Action
Filler, G., Gipson, D. S., Iyamuremye, D., & Ferris, M. (2022) · Advances in Kidney Disease and Health · doi:10.1053/j.akdh.2022.11.001 · Cited by 14
Artificial Intelligence in Digital Health: Issues and Dimensions of Ethical Concerns
Ishengoma, F. (2022) · Innovación y Software · doi:10.48168/innosoft.s8.a48 · Cited by 10
“However, several ethical concerns arise in the healthcare domain when AI systems perform the same tasks as humans [9-12].”
BackgroundA review of machine learning methods for non-invasive blood pressure estimation
Pal, R., Le, J., Rudas, Á., Chiang, J. N., Williams, T. M., Alexander, B., Joosten, A., & Cannesson, M. P. (2024) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-024-01221-7 · Cited by 14
“Additionally, while publicly available datas - ets are advantageous for machine learning, mounting ethical and privacy concerns have underscored the need for stricter regulation of data [ 40, 41 ].”
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“[37] Tackling these concerns requires robust governance frameworks that prioritize fairness, accountability, and transparency while actively involving diverse stakeholders.”
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Representational ethical model calibration
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The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical ReviewPreprint
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“The task of the following pages is to focus on the second of these elements — the ethical, regulatory and legal framework — by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively (Char, Shah, & Magnus, 2018).”
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The state of the art in artificial intelligence and digital pathology in prostate cancer
Ni, H. M., Kouzy, R., Sabbagh, A., Rooney, M. K., Feng, J., Castillo, S. P., Gadoue, S. M., Kouzi, Z. E., Hoffman, K. E., Yuan, Y., et al. (2025) · Nature Reviews Urology · doi:10.1038/s41585-025-01070-2 · Cited by 13
“The more central the role of AI in health care becomes, the greater the imperative for the community to ensure that AI is bound by the same ethical principles that guide clinicians 139 .”
Tappeiner, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14176307 · Cited by 15
Abuarqoub, D. A., & Mutahar, M. (2025) · BioMedInformatics · doi:10.3390/biomedinformatics5040069 · Cited by 12
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Pratt, B., Parker, M. J., & Bull, S. (2022) · Journal of Empirical Research on Human Research Ethics · doi:10.1177/15562646221118127 · Cited by 13
“As Char, Shah, and Magnus (2018) note, “ health care delivery already varies by race.”
BackgroundImproving Diagnostics with Deep Forest Applied to Electronic Health Records
Khodadadi, A., Bousejin, N. G., Molaei, S., Chauhan, V. K., Zhu, T., & Clifton, D. A. (2023) · Sensors · doi:10.3390/s23146571 · Cited by 15
“Patient Forest can accurately learn EHR representations and surpass other strong models, especially when the training data is scarce, which is a common difficulty in healthcare domains [103].”
BackgroundSowmya Dongari, M. N. (2023) · Journal of Propulsion Technology · doi:10.52783/tjjpt.v44.i2.131 · Cited by 12
Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients
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Li, M., Xiong, X., Xu, B., & Dickson, C. (2024) · JMIR Formative Research · doi:10.2196/53918 · Cited by 15
“Researchers argue that AI currently lacks the generalized intelligence, emotional skills, reasoning capacity, and societal trust needed to fully replace human physicians [52-56].”
Background
Metadata
- Authors
- Char, D. S., Shah, N. H., & Magnus, D.
- Affiliations
- Stanford Medicine (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2018-03-15
- Volume
- 378
- Issue
- 11
- Pages
- 981-983
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC5962261
- OpenAlex
- W2789970635
- Cited by
- 1,857
- References
- 4
- Citations by year
- 2026: 445 · 2025: 309 · 2024: 263 · 2023: 214 · 2022: 162 · 2021: 174 · 2020: 152 · 2019: 95 · 2018: 26
- FWCI
- 29.24
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (K01HG008498)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- machine learning, health care ethics, ethical considerations, algorithmic bias, patient privacy, clinical decision support
- MeSH terms
- Machine Learning, Algorithms, Decision Making, Computer-Assisted, Delivery of Health Care, Ethics, Medical, Humans, Physician-Patient Relations, Codes of Ethics, Healthcare Disparities, Racism
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