ArticlePLOS digital health2022
A systematic review of federated learning applications for biomedical data.
Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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
46 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture.Cell reports. Medicine · 2024Pooled it
- Radiology research platform: automating multicenter radiological research workflows using a cloud-native platform.European radiology · 2026Article
- Article
- The Design and Conduct of Cohort Studies of Peoples With CKD - International Perspectives From iNET-CKD.Kidney international reports · 2026Article
- Mechanisms and integrative machine learning approaches to blood-brain barrier biomarker profiling for personalized ischemic stroke management.Physiological reports · 2026Review
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- Comparative Molecular Insights and Computational Modeling of Multiple Myeloma and Osteosarcoma.International journal of molecular sciences · 2026Review
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Federated learning frameworks: quality and interoperability for biomedical research.NAR genomics and bioinformatics · 2026Review
- A Pragmatic Framework for Federated Learning Risk and Governance in Academic Medical Centers.JMIR AI · 2026Article
- Comparing decentralized machine learning and AI clinical models to local and centralized alternatives: a systematic review.NPJ digital medicine · 2026Article
- Privacy-preserving federated prediction of health outcomes using multi-center survey data.BMC medical research methodology · 2026Article
- Assessing the Evolution and Influence of Medical Open Databases on Biomedical Research and Health Care Innovation: A 25-Year Perspective With a Focus on Privacy and Privacy-Enhancing Technologies.Journal of medical Internet research · 2026Article
- WMRE2030: integrating wearable devices, multi-omics, and artificial intelligence-driven real-time feedback into a daily-scale closed-loop framework for a new era of precision exercise.Frontiers in physiology · 2026Review
- Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness.Frontiers in cardiovascular medicine · 2026Review
- Big multiple sclerosis data network: novel modelling approaches for real-world data analysis.Journal of neurology · 2025Review
- Federated Learning in Public Health: A Systematic Review of Decentralized, Equitable, and Secure Disease Prevention Approaches.Healthcare (Basel, Switzerland) · 2025Review
- Federated Deep Learning Approaches for Detecting Ocular Diseases in Medical Imaging: A Systematic Review.Current medical imaging · 2025Article
- A quantitative analysis of the use of anonymization in biomedical research.NPJ digital medicine · 2025Article
- Scoping review of deep learning research illuminates artificial intelligence chasm in otolaryngology-head and neck surgery.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectivesFederated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology.
methodsWe conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool.
results13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data.
conclusionFederated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code.
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.