ReviewHealthcare analytics (New York, N.Y.)2023
A systematic review of privacy-preserving methods deployed with blockchain and federated learning for the telemedicine.
Review in Healthcare analytics (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.Frontiers in digital health · 2026Pooled it
- Review
- Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.Health care management science · 2026Article
- Comparing decentralized machine learning and AI clinical models to local and centralized alternatives: a systematic review.NPJ digital medicine · 2026Article
- Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review.Frontiers in digital health · 2026Review
- IoT Authentication in Federated Learning: Methods, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2025Article
- Repair, Regeneration, and Replacement, Revisited (Redux).Journal of diabetes science and technology · 2025Article
- Enhancing autism care through remote support: A family-centered approach.World journal of psychiatry · 2025Article
- Effective Integration of Artificial Intelligence and Blockchain Technologies for Empowerment of Drug Discovery and Development.Current drug discovery technologies · 2025Review
- EnDuSecFed: an ensemble approach for privacy preserving Federated Learning with dual-security framework for sustainable healthcare.Frontiers in big data · 2025Article
- Artificial intelligence as a tool in drug discovery and development.World journal of experimental medicine · 2024Review
- Discursive framework for a multi-disease digital health passport in Africa: a perspective.Globalization and health · 2024Review
- A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning.Sensors (Basel, Switzerland) · 2024Article
- Blockchain Revolutionizing in Emergency Medicine: A Scoping Review of Patient Journey through the ED.Healthcare (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance of remote healthcare systems such as telemedicine. Telemedicine effectively provides remote communication, better treatment recommendation, and personalized treatment on demand. It has emerged as the possible future of medicine. From a privacy perspective, secure storage, preservation, and controlled access to health data with consent are the main challenges to the effective deployment of telemedicine. It is paramount to fully overcome these challenges to integrate the telemedicine system into healthcare. In this regard, emerging technologies such as blockchain and federated learning have enormous potential to strengthen the telemedicine system. These technologies help enhance the overall healthcare standard when applied in an integrated way. The primary aim of this study is to perform a systematic literature review of previous research on privacy-preserving methods deployed with blockchain and federated learning for telemedicine. This study provides an in-depth qualitative analysis of relevant studies based on the architecture, privacy mechanisms, and machine learning methods used for data storage, access, and analytics. The survey allows the integration of blockchain and federated learning technologies with suitable privacy techniques to design a secure, trustworthy, and accurate telemedicine model with a privacy guarantee.
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.