From Medical LLMs to Versatile Medical Agents: A Comprehensive Survey

From Medical LLMs to Versatile Medical Agents: A Comprehensive Survey | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 March 2026 V1 Latest version Share on From Medical LLMs to Versatile Medical Agents: A Comprehensive Survey Authors : Yucheng Zhou 0009-0006-9883-5621 [email protected] , Huan Zheng , Dubing Chen , Hongji Yang , Wencheng Han , and Jianbing Shen Authors Info & Affiliations https://doi.org/10.22541/au.177437366.61663600/v1 131 views 107 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The integration of Large Language Models (LLMs) into healthcare has catalyzed a significant technological leap, evolving from text-based Medical LLMs to Multimodal Medical LLMs (MLLMs) capable of interpreting complex clinical imaging. Despite these advancements, current models predominantly function as passive knowledge engines, proficient in answering queries but lacking the autonomy to navigate the dynamic, longitudinal nature of real-world patient care. This limitation has spurred a paradigm shift toward Medical Agents: proactive systems engineered to sense, reason, plan, and execute actions within clinical environments. In this survey, we provide a comprehensive roadmap of this evolutionary trajectory. We first review the foundational architectures and training strategies of state-of-the-art Medical LLMs and MLLMs. Subsequently, we formalize the construction of Medical Agentic Systems, distinguishing between the cognitive frameworks required for independent Single-Agent Systems and the collaborative paradigms of Multi-Agent Systems that simulate multidisciplinary clinical teams. Central to our analysis is the evolution of medical reasoning, which we categorize into three distinct stages: Core Reasoning for internal deliberation, Augmented Reasoning for tool-mediated and multimodal grounding, and Collective Reasoning for distributed medical intelligence. Finally, the survey examines the necessary transition in evaluation methodologies, from static benchmarks to interactive simulations, and discusses pressing open challenges, offering a forward-looking perspective on building reliable, safe, and clinically impactful medical AI. Project sources: https://github.com/ yczhou001/Awesome-Medical-LLM-Agent. Supplementary Material File (from_medical_llms_to_agents.pdf) Download 2.85 MB Information & Authors Information Version history V1 Version 1 24 March 2026 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License Keywords llm reasoning medical agents medical large language models medical multi-agents multimodal ai for healthcare Authors Affiliations Yucheng Zhou 0009-0006-9883-5621 [email protected] View all articles by this author Huan Zheng View all articles by this author Dubing Chen View all articles by this author Hongji Yang View all articles by this author Wencheng Han View all articles by this author Jianbing Shen View all articles by this author Metrics & Citations Metrics Article Usage 131 views 107 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yucheng Zhou, Huan Zheng, Dubing Chen, et al. From Medical LLMs to Versatile Medical Agents: A Comprehensive Survey. Authorea . 24 March 2026. 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