ArticleNature communications2025
Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare.
Article in Nature communications, 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.
- Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.Scientific reports · 2026Article
- Flexible and scalable federated learning with deep feature prompts for digital pathology.NPJ digital medicine · 2026Article
- Ontology- and LLM-based data harmonization for federated learning in healthcare.Frontiers in digital health · 2026Article
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Review
- The ethics of data mining in healthcare: challenges, frameworks, and future directions.BioData mining · 2025Review
- Towards fairness-aware and privacy-preserving enhanced collaborative learning for healthcare.Nature communications · 2025Article
- From data-driven cities to data-driven tumors: dynamic digital twins for adaptive oncology.Frontiers in artificial intelligence · 2025Article
- Balancing privacy and performance in healthcare: A federated learning framework for sensitive data.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
7 authors.
Funding
Abstract
The widespread integration of AI algorithms in healthcare has sparked ethical concerns, particularly regarding privacy and fairness. Federated Learning (FL) offers a promising solution to learn from a broad spectrum of patient data without directly accessing individual records, enhancing privacy while facilitating knowledge sharing across distributed data sources. However, healthcare institutions face significant variations in access to crucial computing resources, with resource budgets often linked to demographic and socio-economic factors, exacerbating unfairness in participation. While heterogeneous federated learning methods allow healthcare institutions with varying computational capacities to collaborate, they fail to address the performance gap between resource-limited and resource-rich institutions. As a result, resource-limited institutions may receive suboptimal models, further reinforcing disparities in AI-driven healthcare outcomes. Here, we propose a resource-adaptive framework for collaborative learning that dynamically adjusts to varying computational capacities, ensuring fair participation. Our approach enhances model accuracy, safeguards patient privacy, and promotes equitable access to trustworthy and efficient AI-driven healthcare solutions.
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.