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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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“Legal and Ethical Considerations : The inability to explain AI decisions complicates issues of liability and informed consent in medical practice [11].”
BackgroundEthics of Writing Personal Statements and Letters of Recommendations with Large Language Models
Hostetter, L. J., Kelm, D. J., & Nelson, D. R. (2024) · ATS Scholar · doi:10.34197/ats-scholar.2024-0038ps · Cited by 12
Data Engineering for Machine Learning in Women's Imaging and Beyond
Chen, C., Chou, S.-H. S., Brattain, L. J., Lehman, C. D., & Samir, A. E. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20464 · Cited by 9
“At the policy level, researchers can use ML models to help resolve disparities and identify areas of needed research [72].”
BackgroundMachine Learning, Its Limitations, and Solutions Over IT
Kenge, R. N. (2020) · International Journal of Applied Research on Information Technology and Computing · doi:10.5958/0975-8089.2020.00009.3 · Cited by 10
ObjectivAIze: Measuring Performance and Biases in Augmented Business Decision Systems
Baudel, T., Verbockhaven, M., Cousergue, V., Roy, G., & Laarach, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85613-7_22 · Cited by 12
Ethical Challenges of Integrating AI into Healthcare
Lehmann, L. S. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_337-1 · Cited by 13
Shen, Y. (2018) · Computing in cardiology · doi:10.22489/cinc.2018.050 · Cited by 9
“Finally, we would like to discuss briefly whether or not deep learning might be suitable to process datasets such as the one provided by the Challenge, especially considering growing ethical concerns [12].”
BackgroundA Human-Centered Approach to Interactive Machine LearningPreprint
Mathewson, K. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.06289 · Cited by 13
“reward misalignment between humans and machines [6, 2, 22, 12].”
Artificial Intelligence and Machine Learning in Healthcare Sector
Chavda, V. P., Patel, K., Patel, S. H., & Apostolopoulos, V. (2023) · Wiley eBooks · doi:10.1002/9781119865728.ch13 · Cited by 11
Meslamani, A. Z. A., Jarab, A. S., & Ghattas, M. A. (2023) · Journal of Medical Economics · doi:10.1080/13696998.2023.2224018 · Cited by 9
“Taking into consideration that ML requires high-quality data 15 , patient confidentiality and privacy must be ensured.”
BackgroundReddy, S., Lebrun, A., Chee, A., & Kalogeropoulos, D. (2024) · Telehealth and Medicine Today · doi:10.30953/thmt.v9.485 · Cited by 9
Adoption of AI in Oncological Imaging: Ethical, Regulatory, and Medical-Legal Challenges
Alì, M., Fantesini, A., Morcella, M. T., Ibba, S., D’Anna, G., Fazzini, D., & Papa, S. (2023) · Critical Reviews™ in Oncogenesis · doi:10.1615/critrevoncog.2023050584 · Cited by 8
Reddy, D. T., Grewal, I., Pinzon, L. F. G., Latchireddy, B., Goraya, S., Alansari, B. A., & Gadwal, A. (2024) · Cureus · doi:10.7759/cureus.61523 · Cited by 9
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Human vs Machine: The Future of Decision-making in Plastic and Reconstructive Surgery
Duran, A., Demiröz, A., Çörtük, O., Ok, B., Özten, M., & Eroğlu, S. (2025) · Aesthetic Surgery Journal · doi:10.1093/asj/sjaf015 · Cited by 8
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“36 Bias, fairness and lack of generalisability may arise from data used to train a machine-learning algorithm, or how those data were collected and annotated, or many other aspects of modelling or research design.”
Shen, S., Qi, W., Li, S., Zeng, J., Liu, X., Zhu, X., Dong, C., Wang, B., Xu, Q., & Cao, S. (2025) · Digital Health · doi:10.1177/20552076251361614 · Cited by 9
“121,122 Implementation challenges at the institutional level pre-sent additional limitations.”
Can AI Bridge or Widen Maternal Health Inequities?
Laguitan, R. V. M., & Bernardino, G. J. D. L. S. (2025) · Public Health Challenges · doi:10.1002/puh2.70119 · Cited by 17
“Without proactive efforts to diversify data by race, gender, geography, and socioeconomic status, such systems risk reinforcing the very inequities they purport to solve [28, 29].”
Human-AI Collaboration in Healthcare
Gurusamy, V., Muniasamy, A., Gauthaman, S. A., & Abdulla, H. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2787-7.ch012 · Cited by 9
Ali, A., Goyal, B., Monescu, V., Dogra, A., Sudhamsu, G., & Hemanth, D. J. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae1933 · Cited by 9
Trends analysis and future study of medical and pharmacy education: a scoping review
Bashirynejad, M., Soleymani, F., Nikfar, S., Zackery, A., Kebriaeezadeh, A., Majdzadeh, R., Fatemi, B., & Zare, N. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08037-2 · Cited by 12
AI-Enabled Precision Nutrition in the ICU: A Narrative Review and Implementation Roadmap
Briassoulis, G. C., & Briassouli, E. (2025) · Nutrients · doi:10.3390/nu18010110 · Cited by 9
“Gender-related biases, and under-representation of older adults, patients with obesity, and rare-disease populations can introduce bias, reducing prediction accuracy and equity across subgroups [24,54,55,57–59].”
Avakian, A., & Barfoot, G. (2026) · BMC Artificial Intelligence · doi:10.1186/s44398-025-00020-7 · Cited by 12
“Without proactive correction through representative datasets, demographic-aware training protocols, and fairness audits, these disparities will be further embedded into clinical workflows [4, 13].”
Wu, Y. (2025) · Academic Medicine · doi:10.1093/acamed/wvaf039 · Cited by 10
Greenberg, J. K., Otun, A., Ghogawala, Z., Yen, P., Molina, C. A., Limbrick, D. D., Foraker, R. E., Kelly, M. P., & Ray, W. Z. (2021) · Global Spine Journal · doi:10.1177/21925682211008424 · Cited by 10
“Furthermore, data assets may not contain adequate representations of minority groups, leading to decreased predictive performance in those populations.(133,134) Recognizing these potential challenges will allow spine surgeons to maximize the ethical use of HIT.”
BackgroundAn Iterative and Collaborative End-to-End Methodology Applied to Digital Mental Health
Boulos, L., Mendes, A., Delmas, A., & Kaadoud, I. C. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.574440 · Cited by 9
“Algorithms could thus be a bad choice for people with missing data (145) [people with low socioeconomic status (146) or those with psychosocial issues (147) for instance].”
BackgroundRight to Contest AI Diagnostics
Ploug, T., & Holm, S. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_267 · Cited by 11
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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