ArticlePLoS computational biology2025
Generative AI mitigates representation bias and improves model fairness through synthetic health data.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms.Journal of evaluation in clinical practice · 2026Review
- Pitfalls and risks of generative AI in machine learning.Patterns (New York, N.Y.) · 2026Review
- Coherent cross-modal generation of synthetic biomedical data to advance multimodal precision medicine.PLoS computational biology · 2026Article
- Trustworthy AI for radar vital signs: detecting and mitigating gender bias in healthcare.Complex & intelligent systems · 2026Article
- A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification.Frontiers in systems biology · 2026Review
- Reimagining paediatric care: technology, trust, and the global movement for child-centred innovation.Frontiers in medicine · 2026Review
- Chemometric Methods Applied to Infrared and Raman Spectroscopy for Arboviruses Diagnosis: A Systematic Review With Meta-Analysis.Analytical science advances · 2025Review
- The Role of Synthetic Data and Generative AI in Breast Imaging: Promise, Pitfalls, and Pathways Forward.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepresented subpopulations, such as those from specific ethnic backgrounds or genders, do not benefit equally from clinical discoveries. Several approaches have been developed to mitigate representation bias, ranging from simple resampling methods, such as SMOTE, to recent approaches based on generative adversarial networks (GAN). However, generating high-dimensional time-series synthetic health data remains a significant challenge. In response, we devised a novel architecture (CA-GAN) that synthesises authentic, high-dimensional time series data. CA-GAN outperforms state-of-the-art methods in a qualitative and a quantitative evaluation while avoiding mode collapse, a serious GAN failure. We perform evaluation using 7535 patients with hypotension and sepsis from two diverse, real-world clinical datasets. We show that synthetic data generated by our CA-GAN improves model fairness in Black patients as well as female patients when evaluated separately for each subpopulation. Furthermore, CA-GAN generates authentic data of the minority class while faithfully maintaining the original distribution of data, resulting in improved performance in a downstream predictive task.
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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.