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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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“Bias can arise at every step of the process, including study design, data collection, data analysis, model building, model evaluation, and even deployment [40, 43, 201].”
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“That enthusiasm, however, is balanced by significant concerns regarding the accuracy, clinical utility, fairness, and generalizability of prediction models based on health records data(Char et al., 2018; Obermeyer and Emanuel, 2016; Rajkomar et al., 2019; Rajkomar et al., 2018; Shah et al., 2018).”
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Security, privacy, and information-sharing aspects of healthcare artificial intelligence
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“It is now widely acknowledged that AI technologies can absorb harmful biases from the data used to train ML algorithms (Char et al., 2018).”
Huang, Y., Liu, Y., Steel, P. A. D., Axsom, K., Lee, J. R., Tummalapalli, S. L., Wang, F., Pathak, J., Subramanian, L., & Zhang, Y. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab203 · Cited by 30
Big data, machine learning, and population health: predicting cognitive outcomes in childhood
Bowe, A. K., Lightbody, G., Staines, A. S., & Murray, D. M. (2022) · Pediatric Research · doi:10.1038/s41390-022-02137-1 · Cited by 33
Task Similarity Estimation Through Adversarial Multitask Neural Network
Zhou, F., Shui, C., Abbasi, M., Robitaille, L.-É., Wang, B., & Gagné, C. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3028022 · Cited by 29
Allareddy, V., Oubaidin, M., Rampa, S., Venugopalan, S. R., Elnagar, M. H., Yadav, S., & Lee, M. K. (2023) · Orthodontics and Craniofacial Research · doi:10.1111/ocr.12721 · Cited by 31
Weidener, L., & Fischer, M. (2024) · JMIR AI · doi:10.2196/51204 · Cited by 35
Aldhafeeri, F. M. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01052-w · Cited by 35
“For instance, patient confidentiality is a critical concern because AI algorithms process and analyze sensitive medical images [4].”
BackgroundAfrifa‐Yamoah, E., Adua, E., Peprah‐Yamoah, E., Anto, E. O., Opoku‐Yamoah, V., Acheampong, E., Macartney, M. J., & Hashmi, R. (2024) · Chronic Diseases and Translational Medicine · doi:10.1002/cdt3.137 · Cited by 34
“147 Moreover, the opacity of ML models challenges informed consent and makes it di ffi cult for clinicians to explain ML ‐ aided diagnoses, disrupting doctor ‐ patient trust and shared decision ‐ making.”
Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives
Zbrzezny, A. M., & Krzywicki, T. (2025) · Applied Sciences · doi:10.3390/app15147856 · Cited by 37
A Hands-On Introduction to Data Science
Shah, C. S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108560412 · Cited by 28
Hossain, M. A., Ferdousi, R., & Alhamid, M. F. (2020) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2020.07.003 · Cited by 28
Khan, N. C., Perera, C. M., Dow, E. R., Chen, K., Mahajan, V. B., Mruthyunjaya, P., Diana, V., Leng, T., & Myung, D. J. (2022) · Diagnostics · doi:10.3390/diagnostics12071714 · Cited by 33
“The issue of racial bias in AI has been identified in multiple papers in the literature [55,56] and has the potential to lead to the development of AI models that perform well in the patient populations on which they are trained (usually Caucasian populations) and to underperform in other patient groups, leading to in…”
BackgroundA global perspective on data powering responsible AI solutions in health applications
Rudd, J., & Igbrude, C. (2023) · AI and Ethics · doi:10.1007/s43681-023-00302-8 · Cited by 28
“A key step will be determining how to ensure that they are — whether by means of policy enactment, programming approaches, task-force work, or a combination of these strategies [39].””
BackgroundTowards a Multi-Stakeholder process for developing responsible AI governance in consumer health
Rozenblit, L., Price, A. I., Solomonides, A. E., Joseph, A. L., Srivastava, G., Labkoff, S. E., deBronkart, D., Singh, R., Dattani, K., López-González, M., et al. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105713 · Cited by 27
Kahraman, F., Aktaş, A., Bayrakceken, S., Çakar, T. E., Tarcan, H. S., Bayram, M. B., Durak, B., & Ülman, Y. I. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1428396 · Cited by 41
Shao, Y., Lv, X.-Y., Ying, S., & Guo, Q. (2024) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2912404 · Cited by 30
“Ensuring the ethical use of AI in biomedical research requires adherence to established guidelines and ongoing dialogue between researchers, ethicists, and policymakers [114].”
Digital Phenotyping: Ethical Issues, Opportunities, and Threats
Stanghellini, G., & Leoni, F. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00473 · Cited by 27
Machine learning in the EU health care context: exploring the ethical, legal and social issues
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“( Char et al., 2018) Third, even if it is convenient that physicians ’ skill sets include collaborating with and managing AI devices that aggregate big data (Wartman & Combs, 2019), one cannot ignore the fact that it will be hard for physicians to acquire all the technical capacities needed to…”
BackgroundInfluentialBeaulieu, T., Knight, R., Nolan, S., Quick, O., & Ti, L. (2020) · The American Journal of Drug and Alcohol Abuse · doi:10.1080/00952990.2020.1817466 · Cited by 28
“Another ongoing challenge relates to the protection of personal health data (134,148).”
BackgroundInfluentialAppropriate use of machine learning in healthcare
Ozaydin, B., Berner, E. S., & Cimino, J. J. (2021) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2021.100041 · Cited by 30
Wu, C., Hwang, M., Huang, T.-H., Chen, Y. J., Chang, Y.-J., Ho, T.-H., Huang, J., Hwang, K., & Ho, W. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04000-2 · Cited by 27
“In recent years, artificial intelligence and machine learning have developed vigorously, and have a considerable impact in the medical field, including smart diagnosis, medical image processing and classification, drug development testing and nutrition recommendations, etc. [7].”
A Process Framework for Ethically Deploying Artificial Intelligence in Oncology
Hantel, A., Clancy, D. D., Kehl, K. L., Marron, J. M., Allen, E. M. V., & Abel, G. A. (2022) · Journal of Clinical Oncology · doi:10.1200/jco.22.01113 · Cited by 27
Exploring explainable AI features in the vocal biomarkers of lung disease
Chen, Z., Liang, N., Li, H., Zhang, H., Li, H., Yan, L., Hu, Z., Chen, Y., Zhang, Y., Wang, Y., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108844 · Cited by 26
Ahluwalia, M., Abdalla, M., Sanayei, J., Seyyed-Kalantari, L., Hussain, M., Ali, A., & Fine, B. (2023) · Radiology: Artificial Intelligence · doi:10.1148/ryai.220270 · Cited by 26
Bridging Health Disparities in the Data-Driven World of Artificial Intelligence: A Narrative Review
Murphy, A., Bowen, K., Naqa, I. M. E., Yoga, B., & Green, B. L. (2024) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-024-02057-2 · Cited by 27
“Many datasets used to train AI algorithms are based on human decisions that may be already biased [10, 32].”
Ethics dumping in artificial intelligence
Bélisle-Pipon, J.-C., & Victor, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1426761 · Cited by 26
“However, ethical issues like eroding human decision autonomy, entrenching biases from historical data, and disrupting professional role boundaries often remain underexplored during acquisition and piloting phases (Char et al., 2018).”
Algorithmic accountabilities and health systems: A review and sociomaterial approach
Donia, J. (2025) · Big Data & Society · doi:10.1177/20539517251334099 · Cited by 28
“Commentators in healthcare have similarly worried about how algorithms will affect the patient – provider relationship, including notions of responsibility, con fi dential-ity, liability, and other tenets of professional ethics (Char et al., 2018).”
Levy, J., Lima, J. F., Miller, M. W., Freed, G. L., O’Malley, A. J., & Emeny, R. T. (2022) · Frontiers in Medical Technology · doi:10.3389/fmedt.2022.926667 · Cited by 27
Alatrany, A. S., Hussain, A. J., Mustafina, J., & Al‐Jumeily, D. (2022) · IEEE Access · doi:10.1109/access.2022.3182543 · Cited by 26
“New research [75-77], has dwelled and dived into the potential ethical issues which may come to light when using machine learning models.”
BackgroundRamaekers, M., Viviers, C. G. A., Janssen, B. V., Hellström, T. A. E., Ewals, L. J. S., van der Wulp, K., Nederend, J., Jacobs, I., Pluyter, J. R., Mavroeidis, D., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12134209 · Cited by 26
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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