ArticleNPJ digital medicine2023
An adversarial training framework for mitigating algorithmic biases in clinical machine learning.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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.
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Who cites it
69 citing papers in PubMed.
- Using classification and regression trees (CART) to investigate unknown ethnicity records in NHS emergency care data.BMJ open · 2026Article
- Large Language Models for Optimizing Patient Recruitment Decisions in Voice Data Generation Projects.Laryngoscope investigative otolaryngology · 2026Article
- Review
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Test-fairness deep learning with influence score.PLOS digital health · 2026Article
- Identifying and mitigating bias in multiple aspects of modern clinical research.Communications medicine · 2026Review
- Attention guided fair artificial intelligence modeling for skin cancer diagnosis.NPJ digital medicine · 2026Article
- Training AI Models for Aesthetic Facial Evaluation: Focused Review and Framework to Mitigate Homogenizing Bias.Journal of medical Internet research · 2026Review
- Photoacoustic device fingerprints induce bias in deep learning models.Scientific reports · 2026Article
- Integrating mission-aligned value with cost to assess the economic impact of AI in healthcare.NPJ digital medicine · 2026Review
- Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.Scientific reports · 2026Article
- Mitigating bias in multilabel medical text classification: a cooperative training framework with dynamic debiasing.Bioinformatics (Oxford, England) · 2026Article
- Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care.Patterns (New York, N.Y.) · 2026Review
- Mitigating algorithmic unfairness arising from forgetfulness of medical records in clinical artificial intelligence.Nature communications · 2026Article
- Fairness analysis of machine learning predictions of aggression in acute psychiatric care.Npj mental health research · 2026Article
- When Artificial Intelligence Fails the Future: How Unchecked Artificial Intelligence Could Amplify Inequality in Medical Education.Plastic and reconstructive surgery. Global open · 2026Article
- Reducing demographic bias in biomedical machine learning for cancer detection using cfDNA methylation.Genome biology · 2026Article
- Integration of fairness-awareness into clinical language processing models.Communications medicine · 2026Article
- Evaluation and improvement of algorithmic fairness for COVID-19 severity classification using Explainable Artificial Intelligence-based bias mitigation.JAMIA open · 2026Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection. We demonstrate this proposed framework on the real-world task of rapidly predicting COVID-19, and focus on mitigating site-specific (hospital) and demographic (ethnicity) biases. Using the statistical definition of equalized odds, we show that adversarial training improves outcome fairness, while still achieving clinically-effective screening performances (negative predictive values >0.98). We compare our method to previous benchmarks, and perform prospective and external validation across four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.
Identifiers
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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.