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Implementing Machine Learning in Health Care — Addressing Ethical Challenges
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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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Self-citationDesigning Expert-Augmented Clinical Decision Support Systems to Predict Mortality Risk in ICUs
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“[2], and decision-making in healthcare [3].”
Application of Artificial Intelligence in Shoulder Pathology
Cheng, C., Liang, X., Guo, D., & Xie, D. (2024) · Diagnostics · doi:10.3390/diagnostics14111091 · Cited by 6
“Compliance with regulatory requirements, such as data privacy and security and medical device regulations, adds another challenge to AI deployment for developers and healthcare organizations [16,79].”
Use of genetic risks in pediatric organ transplantation listing decisions: A national survey
Graf, M., Char, D. S., Hanson‐Kahn, A., & Magnus, D. C. (2019) · Pediatric Transplantation · doi:10.1111/petr.13402 · Cited by 6
Self-citationArtificial intelligence in radiation oncology: How far have we reached?
Chufal, K. S., Ahmad, I., & Chowdhary, R. L. (2023) · International Journal of Molecular and Immuno Oncology · doi:10.25259/ijmio_32_2022 · Cited by 6
Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics
Hsu, W. W. Y., Baumgärtner, C., Deserno, T. M., & Section Editors for the IMIA Yearbook Section on Sensors, S. (2020) · Yearbook of Medical Informatics · doi:10.1055/s-0040-1702004 · Cited by 6
The Emperor’s New Clothes? Transparency and Trust in Machine Learning for Clinical Neuroscience
Starke, G. (2021) · Advances in neuroethics · doi:10.1007/978-3-030-64590-8_14 · Cited by 20
Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment EffectsPreprint
Jeong, S., & Namkoong, H. (2020) · arXiv (Cornell University) · Cited by 6
Artificial Intelligence‐Enabled ECG : Physiologic and Pathophysiologic Insights and Implications
Kashou, A. H., Adedinsewo, D. A., Siontis, K. C., & Noseworthy, P. A. (2022) · Comprehensive physiology · doi:10.1002/cphy.c210001 · Cited by 10
Multimodal Prediction of Breast Cancer Recurrence Assays and Risk of RecurrencePreprint
Howard, F. M., Dolezal, J. M., Kochanny, S. E., Khramtsova, G. F., Vickery, J., Srisuwananukorn, A., Woodard, A., Chen, N., Nanda, R., Perou, C. M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.07.499039 · Cited by 6
Kargarandehkordi, A., & Washington, P. Y. (2023) · medRxiv · doi:10.1101/2023.04.28.23289168 · Cited by 7
“…health to predict psychological and perceived anxiety, there are challenges related to reliability due to limitations in data collection [ 32 ], limited research-only resources [ 33 ], confidentiality of the data and inability to disclose it [ 34 ], and lack of effective commercial licenses [ 35 ].”
BackgroundChatGPT and beyond with artificial intelligence (AI) in health: Lessons to be learned
Thiébaut, R., Hejblum, B. P., Fleur Mougin, A., Tzourio, C., & Richert, L. E. (2023) · Joint Bone Spine · doi:10.1016/j.jbspin.2023.105607 · Cited by 7
Opportunities and challenges of machine learning in transplant-related studies
Kang, J. (2023) · American Journal of Transplantation · doi:10.1016/j.ajt.2023.11.015 · Cited by 6
Carulli, C., Rossi, S. M. P., Magistrelli, L., Annibaldi, A., & Troncone, E. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14030690 · Cited by 5
Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning
Yin, S., & Li, Y.-H. (2025) · World Journal of Psychiatry · doi:10.5498/wjp.v15.i3.103321 · Cited by 7
Ryan, K., Kasun, M., Roberts, L. W., & Kim, J. P. (2025) · Big Data & Society · doi:10.1177/20539517251343853 · Cited by 5
“…that utilize and store health data (Iacobucci, 2017; Price and Cohen, 2019), and the importance of explainable AI (Amann et al., 2020); such fi ndings have promoted frameworks for use in performing systematic ethical appraisals of the development process of clinical AI (Char et al., 2018, 2020).”
Barry, B. A., Sharp, R. R., & McGowan, M. L. (2025) · International Journal of Ethics Education · doi:10.1007/s40889-025-00218-3 · Cited by 10
Makrygiannakis, M. A., & Kaklamanos, E. G. (2025) · European Journal of Orthodontics · doi:10.1093/ejo/cjaf085 · Cited by 5
“Studies indicate that models trained on limited or unbalanced datasets tend to perform worse for underrepresented groups or rare cases [39, 40].”
Artificial superintelligence alignment in healthcare
Ueda, D., Walston, S. L., Kurokawa, R., Saida, T., Honda, M., Iima, M., Watabe, T., Yanagawa, M., Nishioka, K., Sofue, K., et al. (2025) · Japanese Journal of Radiology · doi:10.1007/s11604-025-01907-1 · Cited by 8
“However, an ASI lacking standards of compassion might suggest an approach optimal for survival but oblivious to a patient’s subjective with modern ethical values like fairness and equity [ 25 , 33 , 34 ].”
Artificial Intelligence as a Tool in the Diagnosis of Bladder Cancer: A Narrative Review
Ishtiaq, S., & Farouk, K. (2025) · Cureus · doi:10.7759/cureus.96958 · Cited by 5
Negussie, M. A., Sanchez, N., Sidiq, S. A., Trvalik, A., Baettig, E., Özkök, S., & Umair, M. (2025) · Communications Medicine · doi:10.1038/s43856-025-01179-w · Cited by 7
“The result is a hesitancy to adopt AI solutions due to fear of breaching patient privacy or ethical standards 53 .”
AI in Esophageal Motility Disorders: Systematic Review of High-Resolution Manometry Studies
Gong, E. J., Bang, C. S., Lee, J. J., & Baik, G. H. (2025) · Journal of Medical Internet Research · doi:10.2196/85223 · Cited by 7
“The ethical implications of AI implementation in HRM diagnostic practice deserve careful consideration [52].”
Backcasting the Trust Gap: A Strategic Road Map for Clinician Adoption of AI Diagnostics by 2040
Yu, Y. (2026) · Journal of Medical Internet Research · doi:10.2196/94234 · Cited by 7
“Current trends as part of the problem—the prevailing trajectory, releasing increasingly powerful LLMs into clinical contexts without any verification infrastructure, increases the rate of hallucination-driven errors, actively eroding the trust that adoption requires [6].”
Murdoch, B., Jandura, A., & Caulfield, T. (2022) · Canadian Journal of Bioethics · doi:10.7202/1094696ar · Cited by 7
“Notably, they can be prone to certain types of errors and biases (11-14), and often cannot be easily supervised by human medical professionals.”
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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