ArticleJournal of medical ethics2025
Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives.
Article in Journal of medical ethics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- Ethical implications of the use of AI-based technologies for medical image classification systems in screening: a qualitative systematic review.Health technology assessment (Winchester, England) · 2026Pooled it
- Occupational Health Applied Infodemiological Studies of Nutritional Diseases and Disorders: Scoping Review with Meta-Analysis.Nutrients · 2023Pooled it
- A Multidisciplinary Model for Risk Management and Detection of Ageist Bias in Healthcare Systems in the Era of Artificial Intelligence.Healthcare (Basel, Switzerland) · 2026Article
- Medical artificial intelligence and the right to health.BMC medical ethics · 2026Article
- Artificial intelligence and medical futility.BMC medical informatics and decision making · 2026Review
- Fairness analysis of machine learning predictions of aggression in acute psychiatric care.Npj mental health research · 2026Article
- Mitigating Algorithmic Bias in Cancer Site Classification Models.JCO clinical cancer informatics · 2026Article
- Review
- Leveraging imperfection with MEDLEY: a multi-model approach harnessing bias in medical AI.Frontiers in artificial intelligence · 2026Article
- Co-creating an Inclusion, Diversity, Equity, and Accessibility (IDEA) Strategy in a Pan-Canadian Health Data Research Network.International journal of population data science · 2026Article
- Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review.Journal of medical Internet research · 2025Review
- Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals' perspectives in the UK.BMC medical ethics · 2025Article
- Ethical and social considerations of applying artificial intelligence in healthcare-a two-pronged scoping review.BMC medical ethics · 2025Article
- Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists.Journal of medical Internet research · 2025Article
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- Algorithmic bias in public health AI: a silent threat to equity in low-resource settings.Frontiers in public health · 2025Article
- Biases in AI: acknowledging and addressing the inevitable ethical issues.Frontiers in digital health · 2025Review
- Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities.American heart journal plus : cardiology research and practice · 2024Article
- Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients.Applied clinical informatics · 2024Article
- Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices.Journal of personalized medicine · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race).
objectivesOur objectives are to canvas the range of strategies stakeholders endorse in attempting to mitigate algorithmic bias, and to consider the ethical question of responsibility for algorithmic bias. METHODOLOGY: The study involves in-depth, semistructured interviews with healthcare workers, screening programme managers, consumer health representatives, regulators, data scientists and developers.
resultsFindings reveal considerable divergent views on three key issues. First, views on whether bias is a problem in healthcare AI varied, with most participants agreeing bias is a problem (which we call the bias-critical view), a small number believing the opposite (the bias-denial view), and some arguing that the benefits of AI outweigh any harms or wrongs arising from the bias problem (the bias-apologist view). Second, there was a disagreement on the strategies to mitigate bias, and who is responsible for such strategies. Finally, there were divergent views on whether to include or exclude sociocultural identifiers (eg, race, ethnicity or gender-diverse identities) in the development of AI as a way to mitigate bias. CONCLUSION/SIGNIFICANCE: Based on the views of participants, we set out responses that stakeholders might pursue, including greater interdisciplinary collaboration, tailored stakeholder engagement activities, empirical studies to understand algorithmic bias and strategies to modify dominant approaches in AI development such as the use of participatory methods, and increased diversity and inclusion in research teams and research participant recruitment and selection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.