ArticleNPJ digital medicine2025
A scoping review of the governance of federated learning in healthcare.
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 17 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
17 citing papers in PubMed.
- Organoids in Precision Radiotherapy: Methodological Foundations, Tumor-Specific Evidence, and Translational Roadmaps.Cancer medicine · 2026Review
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
- Artificial intelligence and pediatric imaging data: ethical strategies for learning and collaboration.Pediatric radiology · 2026Review
- Eleven quick tips for Biomedical Federated Learning.PLoS computational biology · 2026Article
- Blockchain technology for secure and efficient nephrology data management.Clinical kidney journal · 2026Review
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- The next frontier in sepsis: connected ICU data for real-world clinical decision making.Intensive care medicine · 2026Review
- Federated Learning in Healthcare Ethics: A Systematic Review of Privacy-Preserving and Equitable Medical AI.Healthcare (Basel, Switzerland) · 2026Review
- Federated learning for fair autism spectrum disorder screening across age-heterogeneous populations.Frontiers in digital health · 2026Article
- A simulation study of differentially private federated learning with zero-trust policy screening for medical IoT devices.Frontiers in digital health · 2026Article
- Tripartite evolutionary game analysis of medical data governance: interactions among government, medical institutions, and third-party assessment agencies.Frontiers in public health · 2026Article
- Integrating AI tools into dynamic digital health ecosystems for personalized cancer screening.International journal of surgery (London, England) · 2026Article
- Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review.Frontiers in digital health · 2026Review
- Advances and Challenges in Drug Screening for Cancer Therapy: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2025Review
- Federated Learning in Public Health: A Systematic Review of Decentralized, Equitable, and Secure Disease Prevention Approaches.Healthcare (Basel, Switzerland) · 2025Review
- The Role of Artificial Intelligence in Herpesvirus Detection, Transmission, and Predictive Modeling: With a Special Focus on Marek's Disease Virus.Pathogens (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In healthcare, federated learning (FL) is emerging as a methodology to enable the analysis of large and disparate datasets while allowing custodians to retain sovereignty. While FL minimises data-sharing challenges, concerns surrounding ethics, privacy, maleficent use, and harm remain. These concerns can be managed by effective data governance. Data governance specifies procedural, relational, and structural mechanisms governing how data is captured, shared, and analysed, the resultant models and their use. However, limited insights exist on the optimal governance of this emerging technology. This study aims to develop a consolidated framework of the data governance mechanisms for FL in healthcare. A scoping review was performed, using deductive and inductive analysis of 39 articles. The framework includes twelve procedural, ten relational, and twelve structural mechanisms. The framework directs researchers to examine how to enact each mechanism and provides practitioners with insights into the mechanism to consider when governing FL.
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.