ArticleNPJ digital medicine2024
An in-depth evaluation of federated learning on biomedical natural language processing for information extraction.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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.
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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.Frontiers in digital health · 2026Pooled it
- CURENet: combining unified representations for efficient chronic disease prediction.Health information science and systems · 2026Article
- Review
- F-Transformer: a federated transformer for efficient and privacy-preserving sequence generation.Scientific reports · 2026Article
- Multidisciplinary expert evaluation of large language models on questions regarding bariatric surgery: a comparative analysis of ERNIE Bot 4.0, ChatGPT-4, Claude 3 Opus, and Gemini Pro.Scientific reports · 2026Article
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- Hybrid rule-based and on-premises LLM pipeline for extracting CMR and CPET metrics from free-text reports in repaired tetralogy of Fallot.medRxiv : the preprint server for health sciences · 2026Article
- Automated Identification of Cancer-Associated Thrombosis Events via Natural Language Processing: A Systematic Review of the Literature.Bleeding, thrombosis and vascular biology · 2026Article
- Comparison of ChatGPT and DeepSeek on a Standardized Audiologist Qualification Examination in Chinese: Observational Study.JMIR formative research · 2025Observational
- MMRAG: multi-mode retrieval-augmented generation with large language models for biomedical in-context learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A scoping review of the governance of federated learning in healthcare.NPJ digital medicine · 2025Article
- FedOcw: optimized federated learning for cross-lingual speech-based Parkinson's disease detection.NPJ digital medicine · 2025Article
- Perspectives and Experiences With Large Language Models in Health Care: Survey Study.Journal of medical Internet research · 2025Article
- Privacy preserving strategies for electronic health records in the era of large language models.NPJ digital medicine · 2025Article
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
- Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies.Frontiers in neurology · 2025Article
- Addressing Generalizability in Clinical Named Entity Recognition: Federated Learning or Large Language Models?: A Case Study on Visual Acuity Extraction from US and UK Eye Institutes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks encompassing 8 corpora using 6 LMs. Our results show that: (1) FL models consistently outperformed models trained on individual clients' data and sometimes performed comparably with models trained with polled data; (2) with the fixed number of total data, FL models training with more clients produced inferior performance but pre-trained transformer-based models exhibited great resilience. (3) FL models significantly outperformed pre-trained LLMs with few-shot prompting.
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.