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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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Tupasela, A., & Nucci, E. D. (2020) · AI & Society · doi:10.1007/s00146-020-00945-9 · Cited by 42
Doctor-patient interactions in the age of AI: navigating innovation and expertise
Hryciw, B. N., Fortin, Z., Ghossein, J., & Kyeremanteng, K. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1241508 · Cited by 41
“These errors can arise from biases in the training data or limitations in the AI’s understanding of complex medical scenarios (8, 9).”
BackgroundAbid, A., Murugan, A., Banerjee, I., Purkayastha, S., Trivedi, H., & Gichoya, J. W. (2023) · JMIR Medical Education · doi:10.2196/46500 · Cited by 43
Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence
Olawade, D. B., Weerasinghe, K., Mathugamage, M. D. D. E., Odetayo, A., Aderinto, N., Teke, J., & Boussios, S. (2025) · Medicina · doi:10.3390/medicina61030433 · Cited by 53
“AI algorithms, despite their transformative potential in ophthalmology, can inadvertently introduce biases that lead to disparities in patient care [111].”
Mohammed, S., & Malhotra, N. (2025) · BenchCouncil Transactions on Benchmarks Standards and Evaluations · doi:10.1016/j.tbench.2025.100215 · Cited by 53
Big data and data processing in rheumatology: bioethical perspectives
de Lara, A. M., & Peláez-Ballestas, I. D. P. (2020) · Clinical Rheumatology · doi:10.1007/s10067-020-04969-w · Cited by 41
“However, the introduction of algorithms into medical care means that the central relationship in clinical settings changes from patient–physician to patient—healthcare system [15].”
BackgroundGensheimer, M. F., Aggarwal, S., Benson, K. R. K., Carter, J., Henry, A. S., Wood, D., Soltys, S. G., Hancock, S., Pollom, E. L., Shah, N. H., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa290 · Cited by 41
“Some studies show that being shown ML model predictions can improve physician predictions, but the example of computer-aided diagnosis for mammography suggests this is not a given.(26,44) The ML model could be useful even if not used directly by physicians.”
BackgroundMethodResultSelf-citationUtilizing Artificial Intelligence to Enhance Health Equity Among Patients with Heart Failure
Johnson, A. E., Brewer, L. C., Echols, M. R., Mazimba, S., Shah, R. U., & Breathett, K. K. (2022) · Heart Failure Clinics · doi:10.1016/j.hfc.2021.11.001 · Cited by 41
ASHP Statement on the Use of Artificial Intelligence in Pharmacy
Schutz, N., Olsen, C. A., McLaughlin, A. J., Yi, W. M., Nelson, S. D., Kalichira, A. L., Smith, A. H., Miller, K. A., Le, T., Chaffee, B. W., et al. (2020) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxaa249 · Cited by 43
Heyen, N., & Salloch, S. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00679-3 · Cited by 35
Sultana, A., Zare, M., Luo, H., & Ramakrishna, S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222111788 · Cited by 40
Challenges and recommendations for high quality research using electronic health records
Honeyford, K., Expert, P., Mendelsohn, E., Post, B., Faisal, A. A., Glampson, B., Mayer, E. K., & Costelloe, C. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.940330 · Cited by 49
“This must be carefully considered when assessing data availability, generalisability and applicability of the fi ndings (31).”
BackgroundArtificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review
Salama, V., Godinich, B. M., Geng, Y., Humbert‐Vidan, L., Maule, L., Wahid, K. A., Naser, M. A., He, R., Mohamed, A., Fuller, C. D., et al. (2024) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2024.07.025 · Cited by 52
Karnuta, J. M., Golubovsky, J. L., Haeberle, H. S., Rajan, P. V., Navarro, S. M., Kamath, A. F., Schaffer, J. L., Krebs, V. E., Pelle, D. W., & Ramkumar, P. N. (2019) · The Spine Journal · doi:10.1016/j.spinee.2019.10.007 · Cited by 42
Artificial Intelligence in Healthcare
Abhinav, G. V., & Subrahmanyam, S. N. (2019) · Journal of Drug Delivery and Therapeutics · doi:10.22270/jddt.v9i5-s.3634 · Cited by 40
Hindocha, S., & Badea, C. (2021) · AI and Ethics · doi:10.1007/s43681-021-00089-6 · Cited by 46
Jha, K. K., Jha, R., Jha, A. K., Hassan, M. A. M., Yadav, S. K., & Mahesh, T. R. (2021) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss54238.2021.9683524 · Cited by 52
Estiri, H., Strasser, Z. H., Rashidian, S., Klann, J. G., Wagholikar, K. B., McCoy, T. H., & Murphy, S. N. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac070 · Cited by 43
Betzler, B. K., Yang, H. H. S., Thakur, S., Yu, M., Quek, T. C., Soh, Z. D., Lee, G., Tham, Y. C., Wong, T. Y., Rim, T. H., et al. (2021) · JMIR Medical Informatics · doi:10.2196/25165 · Cited by 39
“The introduction of artificial intelligence in clinical medicine has brought about ethical concerns, of which one is problematic decision-making by algorithms that reflect biases that are inherent in the data used to train these algorithms [23].”
BackgroundArtificial Intelligence in the Management of Anterior Cruciate Ligament Injuries
Corban, J. P. A. H., Lorange, J.-P., Laverdière, C., Khoury, J., Rachevsky, G., Burman, M. L., & Martineau, P. A. (2021) · Orthopaedic Journal of Sports Medicine · doi:10.1177/23259671211014206 · Cited by 47
“Furthermore, an inherent property of ML algorithms is their capacity to acquire knowledge, which may lead to an unexpected agency or authority in medical decision making.(8) According to Char et al,(8) this degree of autonomy would not only require stricter regulation but also may necessitate a change in how we presen…”
BackgroundInfluentialEthical considerations in AI-enhanced medical decision support systems: A review
Olorunsogo, T. O., Adenyi, A. O., Okolo, C. A., & Babawarun, O. (2024) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2024.11.1.0061 · Cited by 48
Artificial intelligence in clinical decision support and the prediction of adverse events
Oei, S. P., Bakkes, T. H. G. F., Mischi, M., Bouwman, R. A., van Sloun, R. J. G., & Turco, S. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1403047 · Cited by 49
Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure
Bächtiger, P., Plymen, C. M., Pabari, P., Howard, J. P., Whinnett, Z. I., Opoku, F., Janering, S., Faisal, A. A., Francis, D. P., & Peters, N. S. (2020) · Cardiac failure review · doi:10.15420/cfr.2019.14 · Cited by 41
“This lack in interpretability of AI models marks a challenge to adoption, which is understandable when considering the risk of hidden biases in training datasets being learned by models whose output can exhibit discrimination without us realising.(68) HF transcends all socioeconomic and cultural divides, thus requirin…”
BackgroundCurrent applications of artificial intelligence in vascular surgery
Fischer, U. M., Shireman, P. K., & Lin, J. C. (2021) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2021.10.008 · Cited by 40
Machine Learning for the Orthopaedic Surgeon
Alsoof, D., McDonald, C. L., Kuris, E. O., & Daniels, A. H. (2022) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.21.01305 · Cited by 40
“In this algorithm, there was an unsettling tendency for racial bias, as it would predict a Black defendant to be 77% more likely to commit a future crime than a white defendant, even after controlling for prior crimes 61 .”
BackgroundOn Smart Geometric Non-Destructive Evaluation: Inspection Methods, Overview, and Challenges
Jaber, A., Karganroudi, S. S., Meiabadi, M. S., Aminzadeh, A., Ibrahim, H., Adda, M., & Taheri, H. (2022) · Materials · doi:10.3390/ma15207187 · Cited by 40
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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