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From the 1 of 7 linked papers with an AI index.

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7 papers

cs.AI2026

GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning

Lang Cao, Yuhao Shen, Tianyang Luo +3

GuideSkill adds an external reasoning layer that turns disease guidelines into executable functions, allowing large language models to score diagnostic options and improve clinical…

cs.AI2026

MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning

Yuhao Shen, Lang Cao, Simo Du +4

Clinical practice guidelines (CPGs) encode evidence-based decision logic that clinicians apply by evaluating patient variables, conditional criteria, and recommendation rules. Howe…

cs.CV2026

SkinGPT-X: A Self-Evolving Collaborative Multi-Agent System for Transparent and Trustworthy Dermatological Diagnosis

Zhangtianyi Chen, Yuhao Shen, Florensia Widjaja +6

While recent advancements in Large Language Models have significantly advanced dermatological diagnosis, monolithic LLMs frequently struggle with fine-grained, large-scale multi-cl…

cs.CV2026

Trustworthy and Fair SkinGPT-R1 for Democratizing Dermatological Reasoning across Diverse Ethnicities

Yuhao Shen, Zhangtianyi Chen, Yuanhao He +14

The clinical translation of dermatological AI is hindered by opaque reasoning and systematic performance disparities across skin tones. Here we present SkinGPT-R1, a multimodal lar…

cs.CV2026

Towards Trustworthy Dermatology MLLMs: A Benchmark and Multimodal Evaluator for Diagnostic Narratives

Yuhao Shen, Jiahe Qian, Shuping Zhang +3

Multimodal large language models (LLMs) are increasingly used to generate dermatology diagnostic narratives directly from images. However, reliable evaluation remains the primary b…

cs.CV2025

CoTBox-TTT: Grounding Medical VQA with Visual Chain-of-Thought Boxes During Test-time Training

Jiahe Qian, Yuhao Shen, Zhangtianyi Chen +2

Medical visual question answering could support clinical decision making, yet current systems often fail under domain shift and produce answers that are weakly grounded in image ev…