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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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“27 Thus, before considering broader distribution, personal data will have to be anonymized.”
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“Although, as a whole, accuracy is gradually improving with ML techniques, a significant limitation has been the varying interpretability of certain models (particularly deep learning), and, due to their reliance on the data provided, such algorithms are heavily susceptible to bias [14].”
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“There has been active discussion about ethical adoption in AI technology in healthcare applications [7] including protocols in managing patient data during AI development, integration of AI technology [8], legal and regulation [9].”
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Reviewing Federated Machine Learning and Its Use in Diseases Prediction
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Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study
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“Bias can take a number of forms, including a poor fit between the data collected and the research question being asked, data sets that do not adequately represent the target population, and digital tools that may produce disparate effects when applied to different groups [71,72].”
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“Hence, AI tools can be taught to discriminate, reproduce social stereotypes, and underperform in minority groups, an especially risky proposition in the context of healthcare (Char et al., 2018; Wiens et al., 2019).”
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“Concerns have been raised that systems will underper-form in some patient groups (77), raising ethical problems about systems potentially being biased towards certain ethnic groups (13).”
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“But the desire for accurate artificial-intelligence systems must be balanced with the goal of transparency and interpretability to build trust among healthcare practitioners and patients and ensure the responsible integration of insights into clinical decision-making [18].”
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BackgroundMachine learning for sperm selection
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“Finally, it is also possible that an AI/ML algorithm is intentionally designed in a biased way, e.g. to favor diagnoses which are more pro fi table in certain healthcare systems (Char et al., 2018) or to recommend treatments produced by a particular company.”
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“As healthcare delivery is already biased with regard to decisions about referrals or withdrawals of care, including decisions made through racial discrimination, it will be challenging for algorithms to correct for existing biases in the handling of results.(32) Uncertain and incidental (or secondary) results in clini…”
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“…are already manifesting at multiple levels in medical research ranging from study design, data collection, and real-time data analysis to societal applications of Big Data (Bohannon, 2015a; Char et al., 2018; Forsting, 2017; Garvey, 2018d; Just et al., 2017; Kaiser, 2018; Meyer et al., 2018).”
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“While one must endeavor to hold algorithmic decision-making to the same high standards that guide clinicians (Char, Shah, and Magnus 2018), it is therefore essential that mechanisms be in place to override or to appeal decisions (World Health Organization 2021).”
BackgroundTing, D. S. W., Cheung, C. Y.-L., Nguyen, Q., Sabanayagam, C., Lim, G., Lim, Z. W., Tan, G. S. W., Soh, Y. Q., Schmetterer, L. F., Wang, Y. X., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0097-x · Cited by 85
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“Inherent discrimination occurring within care delivery can be challenging to predict and uncover, and biases could easily transfer over into the design and use of AI algorithms (Leslie et al., 2021; Char et al., 2018).”
BackgroundArtificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges
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“Although many of these challenges are inherent in any biomedical application of AI (Char et al. 2018; Obermeyer and Emanuel 2016; Topol 2019; Vayena et al. 2018), at least five of them acquire particular ethical salience when applied to the human brain and clinical neuroscience more generally.”
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“optimize outcomes based on historically inequitable health-care data [66,69,70].”
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“While the identification and minimaliza-tion of the bias are consistently promoted, novel findings also need to be incorporated to allow continuous optimization towards higher quality standards (32, 33).”
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Artificial Intelligence in Hypertension
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“96 Second, it is still unclear where legal liability lies when ML algorithms result in harm and resolving this will help clinical implementation of ML. Medicine is an ever-changing field, where clinical and operational practices in clinical settings constantly evolve.”
BackgroundTransforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward
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“15,16 In healthcare, such biases can significantly compromise patient safety and the quality of care for marginalized groups.”
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“30 This potential for bias leads us to ask to what extent we should be prepared to accept the outputs of the autonomy algorithm.”
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“Transparency also has implications for biases, as AI technologies have the potential for algorithmic bias, reinforcing discriminatory practices based on data points such as position, experience or race, sex, or other features [19].”
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“However, these algorithms can reflect or amplify problematic biases present in their training data 1–4 .”
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Privacy and ethical challenges in next-generation sequencing
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“implications for the fiduciary relationship between clinician and patient[92].”
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Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine
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“The necessity to obtain not only increasing prediction accuracy but also the interpretation of the solutions determined by ML or Deep Learning arises at the latest with the ethical [65,66], legal [67], psychological [68], medical [69,70], and sociological [71] questions tied to their application.”
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“The concept of physician–patient confidentiality requires that a doctor withholds medical information in line with the patient’s wishes as long as this poses no risk to the patient or others [24].”
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What Are Important Ethical Implications of Using Facial Recognition Technology in Health Care?
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The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review
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Mobile health ethics and the expanding role of autonomy
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Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection
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“General Challenges [25,26] User Data Privacy and Confidentiality User Technology Adoption and Engagement Ethical Constraints”
BackgroundMethod
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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