Adapting large language models (LLMs) to clinical workflows often requires costly fine-tuning or manual prompt and pipeline engineering. We study LLM-guided MAP-Elites evolution as an inference-time alternative for discovering medical decision strategies and provide an implementation repository at https://github.com/univanxx/llm_guided_evo_medical. We formulate urgency triage, interactive consultation, and medical image classification as evolutionary searches over executable artifacts optimized by task-specific fitness functions.
Across all three settings, evolution improves over manually designed baselines under practical constraints. In triage, evolved programs increase Semigran accuracy from $77.3\%$ to $87.1\%$ and emergency recall from $0.60$ to $0.97$, while improving safety-weighted held-out MIMIC-ESI performance. In interactive consultation, evolved policies improve the accuracy--cost frontier across Llama-3, Qwen-3.5, and Gemma-4 and transfer to held-out iCRAFTMD. In PneumoniaMNIST, prompt-only evolution improves frozen MedGemma VLMs while preserving strict JSON outputs. Qualitative analysis shows that the gains come from interpretable program-level mechanisms, calibrated triage boundaries, targeted evidence acquisition, selective commitment, and finding-oriented visual decision rules, rather than superficial prompt rewording alone.
KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs
The University of New South Wales（新南威尔士大学）；University of Göttingen（哥廷根大学）；Macquarie University（麦考瑞大学）；Beijing Intelligent Decision Medical Technology Co. Ltd（北京智决医疗科技有限公司）
Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）
ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs
National Engineering Research Center of Software Engineering, Peking University, China（软件工程国家工程研究中心，北京大学，中国）；School of Computer Science, Peking University, Beijing, China（北京大学计算机学院，北京，中国）；Key Laboratory of High Confidence Software Technologies, Ministry of Education（教育部高可信软件技术重点实验室）；Peking University Information Technology Institute, Tianjin Binhai, China（北京大学信息技术研究所，天津滨海，中国）；School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）
Representation-guided in-context learning for medical image interpretation with multimodal large language models
Schepens Eye Research Institute of Massachusetts Eye and Ear（麻省眼耳医院谢本斯眼科研究所）；Harvard Medical School（哈佛医学院）；Massachusetts Eye and Ear（麻省眼耳医院）；National Taiwan University Hospital（台湾大学医院）；Massachusetts Institute of Technology（麻省理工学院）；Northwestern University（西北大学）；Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University（哈佛大学肯普纳自然与人工智能研究所）；Harvard University（哈佛大学）；Harvard Data Science Initiative, Harvard University（哈佛大学数据科学倡议）
ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series
Hong Kong Baptist University（香港 Baptist 大学）；Shanxi University（山西大学）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海研究院）；Tencent AI Lab（腾讯人工智能实验室）；Manchester Metropolitan University（曼彻斯特 Metropolitan 大学）；The University of Manchester（曼彻斯特大学）
Improving Medical Communication using Rubric-Guided Counterfactual Recommendations
IDSIA, Dalle Molle Institute for Artificial Intelligence（IDSIA，达勒莫利人工智能研究所）；National University of Science and Technology POLITEHNICA Bucharest（科学与技术国家大学POLITEHNICA布加勒斯特）
Explanation-Guided Medical Named Entity Recognition with Stability and Boundary Awareness for Atopic Dermatitis
School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）；Department of Dermatology, Chongqing Traditional Chinese Medicine Hospital（重庆中医医院皮肤科）；Chongqing Health Center for Women and Children（重庆妇女儿童健康中心）
Eye-gaze Guided Multi-modal Alignment for Medical Representation Learning
School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；City University of Hong Kong（香港城市大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；University of Georgia（佐治亚大学）；Massachusetts General Hospital（麻省总医院）
Department of Computer Science, University of Illinois, Chicago, IL, 60607, USA（伊利诺伊大学芝加哥分校计算机科学系）；Indian Institute of Technology, Varanasi, India（印度班加罗尔理工学院）；Department of Computer Science & Engineering, Indraprastha Institute of Information Technology, Delhi, New Delhi, 110020, India（印度德里印度信息技术研究所计算机科学与工程系）
AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation
Fudan University（复旦大学）；Xidian University（西安电子科技大学）；KAIST（韩国科学技术院）；Children’s Hospital of Fudan University（复旦大学附属儿童医院）；Shanghai AI Laboratory（上海人工智能实验室）；Hong Kong Polytechnic University（香港理工大学）
A Generalist Model for Diverse Text-Guided Medical Image Synthesis
Department of Cardiothoracic Surgery（心脏外科部门）；Stanford Medicine（斯坦福医学）；Department of Ophthalmology（眼科部门）；Division of Cardiovascular Surgery（心血管外科分会）；Penn Medicine（宾夕法尼亚医学）
A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
Department of Mechatronics & Industrial Engineering, Chittagong University of Engineering & Technology（吉大港工程与技术大学机电与工业工程系）；Department of Mechanical Engineering, Chittagong University of Engineering & Technology（吉大港工程与技术大学机械工程系）
A Self-Guided Framework for Radiology Report Generation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen University General Hospital（深圳大学总医院）
