LLMs and AI Agents in Biomedical and Health Sciences

Dear Colleagues,

Recent advances in large language models (LLMs) and AI agents are transforming the biomedical and health sciences by enabling systems that can reason, interact, and act across complex, data-rich environments. Unlike earlier task-specific machine learning models, LLMs and agentic AI systems can integrate heterogeneous biomedical data, perform multi-step reasoning, collaborate with humans, and dynamically adapt to evolving clinical and research contexts. These capabilities present unprecedented opportunities for advancing biomedical discovery, clinical decision support, digital health, education, and healthcare operations, while also raising critical challenges related to safety, reliability, interpretability, ethics, and governance. 

This Special Issue focuses on the design, evaluation, and application of LLMs and AI agents in biomedical and health sciences, emphasizing systems that move beyond static prediction toward interactive, goal-directed, and context-aware intelligence. The scope includes foundational methods, system architectures, and real-world deployments spanning clinical care, biomedical research, public health, and health system operations. Topics of interest include LLM-based clinical and scientific reasoning, multimodal and agentic AI systems, retrieval-augmented generation (RAG), tool-using and autonomous agents, human–AI collaboration, and methods for ensuring robustness, transparency, and trustworthiness in high-stakes biomedical settings. 

The purpose of this Special Issue is threefold. First, it aims to provide a dedicated venue for high-quality methodological and applied research on LLMs and AI agents tailored to biomedical and healthcare domains. Second, it seeks to establish best practices for evaluation, validation, and governance of these systems, particularly in safety-critical and ethically sensitive applications. Third, it encourages interdisciplinary contributions that bridge artificial intelligence, medicine, biomedical informatics, health systems engineering, and social sciences. 

This Special Issue complements and extends existing literature on medical AI and biomedical informatics by shifting the focus from isolated predictive models to agentic, interactive, and system-level AI. While prior work has largely emphasized supervised learning and narrow clinical tasks, this issue highlights emerging paradigms centered on reasoning, autonomy, multimodal integration, and continuous human–AI interaction. By bringing together theoretical advances, practical implementations, and real-world evaluations, this Special Issue aims to advance the responsible development and translation of LLMs and AI agents into biomedical research and healthcare practice.

Dr. Zhenhong Hu
Dr. Yingbo Ma
Guest Editors

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• large language models (LLMs)
• agents
• biomedical AI
• healthcare AI
• clinical decision support
• multimodal AI
• retrieval-augmented generation (RAG)
• agentic AI systems
• human–AI collaboration
• trustworthy and explainable AI