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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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“…been focusing on detecting and fixing biases in HMLA, insofar as biases are deemed to be the main cause of health unfairness (Rajkomar et al. 2018; Char 2018; Obermeyer et al. 2019), and the latter is in turn understood mainly as algorithmic discrimination (Ang-win et al. 2016; Hardt et al. 2016).”
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“Future directions also include promoting policy changes that enforce transparency and fairness in data collection and algorithmic processing in healthcare applications [81].”
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Ethical issues in using ambient intelligence in health-care settings
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“More attention needs to be paid to these challenges, which are identified below, in order to increase the efficiency of the ML domain and improve its usability [20,23,25”
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“Algorithms are developed by humans and by nature; there might be some form of bias in the input training data that has to be borne in mind (Char et al., 2018).”
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“Novel ethical challenges, unconsidered by classical bioethics approaches, which focus largely on a dyadic physician-patient relationship, are already emerging with implementations of AI to healthcare, including the enlarging responsibilities of learning healthcare systems and the capture of ‘‘big data’’ required to su…”
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“Ethical dilemmas, such as the potential of human biases when creating computer algorithms, need to be addressed [34].”
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“…p One focuses on the ethical challenges that AI and ML pose, often linked to specific application domains, such as warfare (Russell 2015), medicine (Char, Shah, and Magnus 2018), or labor (Torresen 2018). r The other focuses on the values or principles that should guide AI and ML developments…”
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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