ReviewStatistics in medicine2025
What Is Fair? Defining Fairness in Machine Learning for Health.
Review in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
9 citing papers in PubMed.
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- Improving classification of myocardial infarction with machine learning in a diverse population.American journal of epidemiology · 2026Article
- A pipeline for enabling path-specific causal fairness in observational health data.Proceedings of the ... Conference on Fairness, Accountability, and Transparency · 2026Article
- Beyond protected characteristics: rethinking AI fairness through the lens of structural and social determinants of health.BMJ digital health & AI · 2026Article
- Exploring opportunities to improve health equity with machine learning and artificial intelligence in healthcare epidemiology.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2026Review
- Trustworthy AI for radar vital signs: detecting and mitigating gender bias in healthcare.Complex & intelligent systems · 2026Article
- What Is Fair? Defining Fairness in Machine Learning for Health.Statistics in medicine · 2025Review
- The ethics of data mining in healthcare: challenges, frameworks, and future directions.BioData mining · 2025Review
- We don't need more apps, we need connection: recommender systems as under-explored chance to promote students' mental health at universities.Frontiers in psychology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Ensuring that machine-learning (ML) models are safe, effective, and equitable across all patients is critical for clinical decision-making and for preventing the amplification of existing health disparities. In this work, we examine how fairness is conceptualized in ML for health, including why ML models may lead to unfair decisions and how fairness has been measured in diverse real-world applications. We review commonly used fairness notions within group, individual, and causal-based frameworks. We also discuss the outlook for future research and highlight opportunities and challenges in operationalizing fairness in health-focused applications.
Indexed as
Identifiers
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.