ArticleNPJ digital medicine2025
Improving medical machine learning models with generative balancing for equity and excellence.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Predicting Craniospinal Surgery in Pediatric Achondroplasia: Benchmarking Statistical Inference and Explainable AI Under Rare-Disease Constraints.Annals of biomedical engineering · 2026Article
- A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives.Journal of healthcare informatics research · 2026Review
- Predictive modelling of duodenal stump leakage after gastric cancer and long-term oncological outcomes.Translational cancer research · 2026Article
- Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help.Journal of biomedical informatics · 2026Article
- The portability paradox of foundation models for clinical decision support.NPJ digital medicine · 2026Article
- Predictive performance of artificial intelligence algorithms for gestational diabetes mellitus in pregnant women: a protocol for systematic review and meta-analysis.Systematic reviews · 2026Article
- Harnessing big data and artificial intelligence in transfusion medicine: Opportunities for precision, safety and efficiency.Vox sanguinis · 2026Review
- Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions.World journal of otorhinolaryngology - head and neck surgery · 2026Review
- A novel pipeline for realistic synthetic longitudinal EHR data generation.Research square · 2026Article
- Symptoms and causes of obstacles in AI-based telemonitoring: strategies for system development through a CRISP-DM lens.Frontiers in artificial intelligence · 2026Article
- Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study.BMC infectious diseases · 2025Article
- Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health.Journal of medical Internet research · 2025Article
- Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study.Journal of medical Internet research · 2025Article
- Exploring perceptions of data risks in AI-enabled nursing research: A qualitative study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Applying machine learning to clinical outcome prediction is challenging due to imbalanced datasets and sensitive tasks that contain rare yet critical outcomes and where equitable treatment across diverse patient groups is essential. Despite attempts, biases in predictions persist, driven by disparities in representation and exacerbated by the scarcity of positive labels, perpetuating health inequities. This paper introduces FairPlay, a synthetic data generation approach leveraging large language models, to address these issues. FairPlay enhances algorithmic performance and reduces bias by creating realistic, anonymous synthetic patient data that improves representation and augments dataset patterns while preserving privacy. Through experiments on multiple datasets, we demonstrate that FairPlay boosts mortality prediction performance across diverse subgroups, achieving up to a 21% improvement in F1 Score without requiring additional data or altering downstream training pipelines. Furthermore, FairPlay consistently reduces subgroup performance gaps, as shown by universal improvements in performance and fairness metrics across four experimental setups.
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.