ArticleJournal of healthcare informatics research2021
Federated Learning for Healthcare Informatics.
Article in Journal of healthcare informatics research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 245 papers, 6 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
245 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Impact of Natural Language Processing models on diagnosis and decision-making in healthcare, business, education, and sports: a review.Frontiers in artificial intelligence · 2025Pooled it
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Learning competing risks across multiple hospitals: one-shot distributed algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability.Frontiers in oncology · 2023Pooled it
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- A Systematic Review of Deep Learning and Machine Learning Applications in Longitudinal Multimodal Clinical Data.Journal of healthcare informatics research · 2026Review
- Article
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Theranostic Smart Bionanosensors for Solid Tumors: Advances, Challenges, and Future Directions.Applied biochemistry and biotechnology · 2026Review
- Artificial Intelligence in Pharmaceutical Care:Saudi medical journal · 2026Review
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Evaluating and Mitigating Carbon Dioxide Equivalent Emissions in Stroke Management: A Modeling Study.Journal of the American Heart Association · 2026Article
- Mapping ICU nursing intervention notes to clinical care classification using a large language model: an evaluation study.BMC nursing · 2026Article
- Historical Perspectives, Classification and Diagnostic Approaches of Inborn Errors of Metabolism: A Systematic Review and Meta-Analysis.Metabolites · 2026Review
- Development and External Validation of a Large Language Model-Based Clinical Decision-Support System for Colonoscopy Surveillance Intervals.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026Article
- Artificial Intelligence in Healthcare Administration and Clinical Informatics: A Critical Review and Governance Roadmap.Healthcare (Basel, Switzerland) · 2026Review
- Multi-modal personalized federated learning with adaptive differential privacy for medical image classification and a privacy-preserving approach.Scientific reports · 2026Article
- Mitigating algorithmic unfairness arising from forgetfulness of medical records in clinical artificial intelligence.Nature communications · 2026Article
185 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, "big data." Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated learning, and point out the implications and potentials in healthcare.
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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.