From algorithmic innovation to clinical deployment: A systematic review of methodological gaps limiting federated learning in healthcare
Clinical Snapshot
PICO Framework
| P — Population | Biomedical and life science research studies implementing federated learning methods |
| I — Intervention | Federated learning methodological approaches and implementations |
| C — Comparator | Traditional centralized machine learning approaches (implicit comparison) |
| O — Outcomes | Methodological quality, translational readiness, privacy mechanisms, evaluation design, and clinical deployment feasibility |
Bottom Line
This systematic review examines federated learning methodologies in healthcare, identifying significant gaps between technical innovation and clinical deployment readiness. While 97 studies demonstrate rapid advancement in optimization and privacy mechanisms, critical deficiencies exist in evaluation design and governance frameworks. The 1.15% inclusion rate from over 8,000 records suggests substantial methodological heterogeneity in this emerging field. For Australian healthcare, federated learning shows promise for multi-institutional collaboration while maintaining data sovereignty, particularly relevant for My Health Record and state-based health networks. However, the identified gaps in translational readiness suggest current implementations are insufficient for routine clinical deployment. Healthcare leaders should approach federated learning adoption cautiously, ensuring robust evaluation frameworks and governance structures before implementation. The review's findings support continued research investment while highlighting the need for standardized evaluation methodologies and regulatory frameworks before widespread clinical adoption.
Key Findings
P Value: Not applicable for systematic review
Effect Size: 97 relevant studies identified from 8,412 records (1.15% inclusion rate)
Primary Outcome: Identification of methodological gaps in federated learning implementation for healthcare
Nnt Or Sensitivity: Review identifies rapid innovation in optimization/privacy but limited support for evaluation and governance
Confidence Interval: Not reported in abstract
Clinical Application
Limited by identified methodological gaps in evaluation design and governance frameworks Relevant to Australian Digital Health Strategy and My Health Record privacy requirements; aligns with NHMRC data sharing guidelines and TGA software as medical device regulations Healthcare organizations, biomedical researchers, and health informatics professionals implementing distributed learning systems
Abstract
OBJECTIVE: Federated learning (FL) is a distributed machine learning paradigm designed to enable model training across decentralized data sources without requiring data centralization. This review critically examines FL as a methodological approach in biomedical informatics, summarizing its conceptual foundations, methodological variants, successes, and limitations, and identifying directions for future research. METHODS: We conducted a systematic methodological review following PRISMA 2020 guidelines. Searches were performed in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between January 2017 and March 2025. Articles were included if they proposed, extended, or critically analyzed FL methods for biomedical or life science data. Methods were categorized by federation topology, optimization strategy, data heterogeneity handling, privacy mechanisms, evaluation design, and translational readiness. RESULTS: From 8,412 records, 97 articles met inclusion criteria. Rapid methodological innovation exists in optimization and privacy mechanisms, while support for design-time evaluation and governance remains limited. CONCLUSION: Federated learning represents a significant methodological advance in biomedical informatics but current implementations address only a subset of translational challenges. Future work must integrate study design, evaluation, interpretability, and governance into FL methods.
References
- 1.Vijayasarathy, S. (2026). From algorithmic innovation to clinical deployment: A systematic review of methodological gaps limiting federated learning in healthcare. International Journal of Medical Informatics, 106365. https://doi.org/10.1016/j.ijmedinf.2026.106365
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