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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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Beyond the algorithm: the importance of medical humanities in the age of AI
Paglialonga, M., & Simonetti, C. (2025) · Bioethics News · doi:10.1007/s40592-025-00259-w · Cited by 4
“Char et al. ( 2018 ) suggest that, when used transparently and responsibly, AI can even enhance the relational skills of clinicians, enabling more empathetic care.”
What is happening to laboratory medicine in Denmark?
Jørgensen, P. E. (2018) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2018-0590 · Cited by 4
Ethics by Agreement in Multi-agent Software Systems
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“To this end, there have also been many proposals on how to create ethical AI (Bringsjord et al., 2006; Anderson and Anderson, 2015; Dennis et al., 2016; Conitzer et al., 2017; Char et al., 2018; Heidari et al., 2018).”
BackgroundAchieving Value in Population Health Big Data
Bu, D. D., Liu, S. H., Liu, B., & Li, Y. (2020) · Journal of General Internal Medicine · doi:10.1007/s11606-020-05869-0 · Cited by 4
Using Facial Recognition Tools for Health Assessment
Boczar, D., Avila, F. R., Carter, R. E., Moore, P. A., Giardi, D., Guliyeva, G., Bruce, C. J., McLeod, C. J., & Forte, A. J. (2021) · Plastic and Aesthetic Nursing · doi:10.1097/psn.0000000000000410 · Cited by 4
“tient confidentiality and entity liability (Char et al., 2018; Martinez-Martin, 2019).”
BackgroundMachine Learning Reimagined: The Promise of Interpretability to Combat Bias
Maurer, L. R., Bertsimas, D., & Kaafarani, H. M. A. (2022) · Annals of Surgery · doi:10.1097/sla.0000000000005396 · Cited by 4
Introducing Computer Vision into Healthcare Workflows
Mosquera, C., Lara, M. A. R., Díaz, F. N., Binder, F., & Benítez, S. (2023) · Health informatics · doi:10.1007/978-3-031-17666-1_4 · Cited by 4
Analysis of Automated Healthcare Diagnosis Models Using Machine Learning
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“[5], focused on the incorporation of machine learning into clinical medicine, the challenges that need to be overcome and guidelines to be employed for creating an overall accurate and working model.”
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“In particular, in medicine, machine learning has proven useful in diagnosing cancer [1] and in predicting epidemic outbreaks [2], and is gaining popularity to become the backbone of clinical decision support systems [3, 4].”
BackgroundÜberall, M., & Werner‐Felmayer, G. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0043 · Cited by 4
“Recently, Char et al. (2018) emphasized the crucial importance toaddress ethicswhenimplementingmachine learningin the clinics since biases, such as racial or social biases inherent in the data could harm patients and violate social justice.”
Predictive System for Heart Disease Using a Machine Learning Trained Model
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Medicina de precisión y transhumanismo: una mirada desde la bioética
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Navigating the Ocean of Big Data in Neurocritical Care
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“In addition, we must remember that an ethical and equity-minded perspective must be applied when collecting and implementing data-driven decision making in medicine [21].”
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“Only a few studies have focused on prediction models solely based on home care data [20], and very few studies have implemented and tested a prediction model in practice, mainly due to ethical and economic considerations [21], barriers among professionals, such as trust in the technology [22], and the fact that data o…”
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“Even in successful propositions [12], suspicion may not arise from the tool itself, but from the way it transforms work practices, perhaps for the better, but in directions which open avenues for high uncertainty [15].”
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EVALUATING MACHINE LEARNING ALGORITHMS FOR CERVICAL CANCER PREDICTION: A COMPARATIVE ANALYSIS.Preprint
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Chemistry and Technology Innovation to Advance Green and Sustainable Chemistry
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Data – Knowledge driven machine learning model for cancer pain medication decisions
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“However, realizing this potential necessitates addressing challenges related to dataset curation, algorithm training and validation, integration into clinical workflows, and addressing concerns related to patient privacy and algorithm transparency (30, 31).”
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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