ReviewBMJ health & care informatics2022
Conceptualising fairness: three pillars for medical algorithms and health equity.
Review in BMJ health & care informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 61 citations in OpenAlex.
- The PALLI-AI Framework: An Evidence-Informed Checklist for Designing and Reporting Trustworthy Artificial Intelligence Tools in Palliative and End-of-Life Care.Healthcare (Basel, Switzerland) · 2026Article
- PREFER-IT: A transdisciplinary co-created framework to realise inclusive health AI.PLOS digital health · 2026Article
- Exploring Applications of AI in the Crisis Line Sector: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- A framework for assessing algorithmic discrimination risks in training data: a case of pediatric type 1 diabetes.JAMIA open · 2026Article
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- An empirical study of machine learning robustness and scalability for imbalanced tabular clinical data in emergency and critical care.Scientific reports · 2026Article
- Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review.Public health in practice (Oxford, England) · 2026Review
- Urgent considerations for suicide prevention in the safe and ethical use of artificial intelligence.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2026Article
- Advancing health equity in proactive health management: from data underrepresentation and algorithmic bias to a closed-loop governance framework.Frontiers in public health · 2026Review
- Targeting everyday decision makers in research: early career researcher and patient and public involvement and engagement collaboration in an AI-in-healthcare project.Research involvement and engagement · 2025Article
- Navigating Healthcare AI Governance: the Comprehensive Algorithmic Oversight and Stewardship Framework for Risk and Equity.Health care analysis : HCA : journal of health philosophy and policy · 2025Article
- Developing and Applying the BE-FAIR Equity Framework to a Population Health Predictive Model: A Retrospective Observational Cohort Study.Journal of general internal medicine · 2025Observational
- Exploring the Role of Artificial Intelligence in Smart Healthcare: A Capability and Function-Oriented Review.Healthcare (Basel, Switzerland) · 2025Review
- Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.Briefings in bioinformatics · 2025Article
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- Article
- Survival After Radical Cystectomy for Bladder Cancer: Development of a Fair Machine Learning Model.JMIR medical informatics · 2024Article
- Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor.NPJ digital medicine · 2024Article
- Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study.EClinicalMedicine · 2024Article
- Reexamining the use of race in medical algorithms: the maternal health calculator debate.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesFairness is a core concept meant to grapple with different forms of discrimination and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet, claims to fairness in ML discourses are often vague and contradictory. The response to these issues within the scientific community has been technocratic. Studies either measure (mathematically) competing definitions of fairness, and/or recommend a range of governance tools (eg, fairness checklists or guiding principles). To advance efforts to operationalise fairness in medicine, we synthesised a broad range of literature.
methodsWe conducted an environmental scan of English language literature on fairness from 1960-July 31, 2021. Electronic databases Medline, PubMed and Google Scholar were searched, supplemented by additional hand searches. Data from 213 selected publications were analysed using rapid framework analysis. Search and analysis were completed in two rounds: to explore previously identified issues (a priori), as well as those emerging from the analysis (de novo).
resultsOur synthesis identified 'Three Pillars for Fairness': transparency, impartiality and inclusion. We draw on these insights to propose a multidimensional conceptual framework to guide empirical research on the operationalisation of fairness in healthcare. DISCUSSION: We apply the conceptual framework generated by our synthesis to risk assessment in psychiatry as a case study. We argue that any claim to fairness must reflect critical assessment and ongoing social and political deliberation around these three pillars with a range of stakeholders, including patients.
conclusionWe conclude by outlining areas for further research that would bolster ongoing commitments to fairness and health equity in healthcare.
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What OpenQuestion holds
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.