ReviewPatterns (New York, N.Y.)2024
Privacy preservation for federated learning in health care.
Review in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses 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
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Research on machine learning-based clinical prediction models: a bibliometric analysis.Frontiers in oncology · 2026Pooled it
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- Beyond robotic platforms: artificial intelligence and the emergence of intelligent surgical ecosystems in colorectal surgery.Journal of robotic surgery · 2026Review
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- The Continuity Trap in Data Science Health Research.Journal of medical Internet research · 2026Article
- Quantum-Augmented Federated AI for Adaptive Pharmacogenomic Precision Oncology: A Perspective.ACS pharmacology & translational science · 2026Review
- Eleven quick tips for Biomedical Federated Learning.PLoS computational biology · 2026Article
- Federated Learning in Endodontics: A Framework for Privacy-Preserving Multicentre Artificial Intelligence.International dental journal · 2026Review
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- Leveraging Real-World Data for Clinical Decision Making in Geriatric Oncology.Drugs & aging · 2026Review
- Designing a secure anonymous group-oriented key agreement protocol for the internet of medical things.Scientific reports · 2026Article
- Flexible and scalable federated learning with deep feature prompts for digital pathology.NPJ digital medicine · 2026Article
- Federated multimodal learning for privacy-preserving blood donor profiling and personalized recall strategy optimization.Scientific reports · 2026Article
- Federated Learning in Healthcare: From Research to Real-World Deployment.Annual review of biomedical engineering · 2026Review
- Bridging Data, Semantics, and Clinical Reasoning: A Knowledge Graph Framework for Pediatric Obstructive Sleep Apnea.Children (Basel, Switzerland) · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Artificial Intelligence in Hepatocellular Carcinoma: Current Applications, Clinical Performance, and Barriers to Implementation.Journal of clinical medicine · 2026Review
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Health-FedNet: secure federated learning for chronic disease prediction on MIMIC-III with differential privacy and homomorphic encryption.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing adoption of FL in health care, it is imperative to elucidate the potential risks. We thus summarize privacy-preserving FL literature in this work with special regard to health care. We draw attention to threats and review mitigation approaches. We anticipate this review to become a health-care researcher's guide to security and privacy in FL.
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.