ArticleEthics and information technology2022
Enabling Fairness in Healthcare Through Machine Learning.
Article in Ethics and information technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ethical concerns and strategies for implementing artificial intelligence in healthcare: a review of empirical studies.BMC medical ethics · 2026Pooled it
- What Is Fair? Defining Fairness in Machine Learning for Health.Statistics in medicine · 2025Review
- Exploring the Role of Artificial Intelligence in Smart Healthcare: A Capability and Function-Oriented Review.Healthcare (Basel, Switzerland) · 2025Review
- The role of artificial intelligence in maternal and child health: Progress, controversies, and future directions.PLOS digital health · 2025Review
- What makes clinical machine learning fair? A practical ethics framework.PLOS digital health · 2025Article
- Using Under-Represented Subgroup Fine Tuning to Improve Fairness for Disease Prediction.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2025Article
- Machine Learning Operations in Health Care: A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- Algor-ethics: charting the ethical path for AI in critical care.Journal of clinical monitoring and computing · 2024Article
- Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).Scientific reports · 2024Article
- Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study.EClinicalMedicine · 2024Article
- "Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations.AI & society · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algorithmic decisions unfair relative to the standard fairness metrics in machine learning. In this paper, we defend the permissible use of affirmative algorithms; that is, algorithms trained on diverse datasets that perform better for traditionally disadvantaged groups. Whilst such algorithmic decisions may be unfair, the fairness of algorithmic decisions is not the appropriate locus of moral evaluation. What matters is the fairness of final decisions, such as diagnoses, resulting from collaboration between clinicians and algorithms. We argue that affirmative algorithms can permissibly be deployed provided the resultant final decisions are fair.
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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.