ReviewComputational intelligence and neuroscience2023
A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics.
Review in Computational intelligence and neuroscience, 2023. 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.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms.Journal of evaluation in clinical practice · 2026Review
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Multi-modal federated learning with differential privacy for privacy-preserving healthcare AI.Scientific reports · 2026Article
- FedLiverNet: a federated learning framework for privacy-preserving and efficient liver cancer detection.Scientific reports · 2026Article
- Federated Choquet Regression with LASSO for Outcome Prediction in Multisite Longitudinal Trial Data.ACM transactions on computing for healthcare · 2026Article
- Quantifying energy and accuracy trade-offs of federated learning on wearable health devices.Frontiers in big data · 2026Article
- Privacy-by-Design with Federated Learning will drive future Rare Disease Research.Journal of neuromuscular diseases · 2026Review
- Integrating Knowledge: The Power of Ontologies in Psychiatric Research and Clinical Informatics.Biological psychiatry · 2025Review
- Data linkage multiplies research insights across diverse healthcare sectors.Communications medicine · 2025Review
- Development of a Transfer Learning-Based, Multimodal Neural Network for Identifying Malignant Dermatological Lesions From Smartphone Images.Cancer informatics · 2025Article
- Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions.Computational and structural biotechnology journal · 2025Review
- Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration.Healthcare (Basel, Switzerland) · 2024Review
- Role of artificial intelligence in critical care nutrition support and research.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2024Review
- Getting ready for the European Health Data Space (EHDS): IDERHA's plan to align with the latest EHDS requirements for the secondary use of health data.Open research Europe · 2024Article
- Using artificial intelligence to improve public health: a narrative review.Frontiers in public health · 2023Review
- Harnessing Artificial Intelligence in Cardiology: Advancements in Diagnosis, Treatment, and Patient Care.Heart views : the official journal of the Gulf Heart AssociationReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Healthcare is predominantly regarded as a crucial consideration in promoting the general physical and mental health and well-being of people around the world. The amount of data generated by healthcare systems is enormous, making it challenging to manage. Many machine learning (ML) approaches were implemented to develop dependable and robust solutions to handle the data. ML cannot fully utilize data due to privacy concerns. This primarily happens in the case of medical data. Due to a lack of precise clinical data, the application of ML for the same is challenging and may not yield desired results. Federated learning (FL), which is a recent development in ML where the computation is offloaded to the source of data, appears to be a promising solution to this problem. In this study, we present a detailed survey of applications of FL for healthcare informatics. We initiate a discussion on the need for FL in the healthcare domain, followed by a review of recent review papers. We focus on the fundamentals of FL and the major motivations behind FL for healthcare applications. We then present the applications of FL along with recent state of the art in several verticals of healthcare. Then, lessons learned, open issues, and challenges that are yet to be solved are also highlighted. This is followed by future directions that give directions to the prospective researchers willing to do their research in this domain.
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.