Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review.

Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information. Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction. FL enables decentralized training across client clusters with limited resources, while LLMs effectively process unstructured health care data. By aggregating client-trained models into a global model, FL ensures broader data inclusion while maintaining privacy. Methods: A scoping review was conducted on peer-reviewed publications retrieved from Google Scholar and Semantic Scholar between 2019 and 2024. Results: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 145 articles from PubMed, arXiv, IEEE, and ACL Anthology met the inclusion criteria. Of these, 12 articles were selected for an in-depth review to examine use cases in ADR prediction. We synthesized ADR data sources on structured and unstructured data types, use cases of FL integrated with natural language processing, and open-source frameworks for ADR identifications and predictions. Special attention is given to unstructured ADR prediction using federated learning with large language models, including development and deployment strategies and evaluation metrics. Conclusions: Given the recent emergence of LLM, the integration of FL and LLM for ADR prediction remains in its early stage, with limited documented use cases. This review explored the potential applications and highlighted the advancements of federated learning with large language models in health care research, particularly in ADR prediction. Key focus areas include fine-tuning and merging algorithms, fairness and unbiasedness, implementation challenges, and real-world deployment strategies. By synthesizing current insights, this review aims to lay the groundwork for future research in privacy-preserving and scalable ADR prediction systems.