ArticleCluster computing2022
Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues.
Article in Cluster computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
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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.
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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.
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Who cites it
44 citing papers in PubMed.
- 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
- A Survey on Security Threats and Mitigation Mechanisms for Smart Hospitals in the 6G Era.Sensors (Basel, Switzerland) · 2026Review
- Artificial intelligence in mobile health applications: A comprehensive review of its role in diabetes care.World journal of methodology · 2026Review
- Federated learning for heterogeneous electronic health record systems with cost effective participant selection.Scientific reports · 2026Article
- Revolutionizing endodontics: the impact and innovations of artificial intelligence.BMC oral health · 2026Review
- From 5G smart hospitals to 6G-enabled learning hospitals: a governance-oriented framework for healthcare technology, policy, and responsible deployment.Frontiers in digital health · 2026Review
- Smart FL: meta-learning for federated blood marrow smear classification.Frontiers in oncology · 2026Article
- Structured-to-text ClinicalBERT embeddings with random Forest for heart disease prediction: a proof-of-concept study on the UCI Statlog dataset.Frontiers in artificial intelligence · 2026Article
- IoT Authentication in Federated Learning: Methods, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2025Article
- Personalized medicine and health equity: overcoming cost barriers and ethical challenges.International journal for equity in health · 2025Review
- Explainable Federated Learning for Multi-Class Heart Disease Diagnosis via ECG Fiducial Features.Diagnostics (Basel, Switzerland) · 2025Article
- FedMedSecure: federated few-shot learning with cross-attention mechanisms and explainable AI for collaborative healthcare cybersecurity.Scientific reports · 2025Article
- Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Review
- Leveraging federated learning and edge computing for pandemic-resilient healthcare.Scientific reports · 2025Article
- Federated stochastic gradient averaging ring homomorphism based learning for secure data aggregation in WSN.Scientific reports · 2025Article
- An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.Scientific reports · 2025Article
- Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care.Journal of clinical medicine · 2025Review
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.
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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.