Problem
Research questions and friction points this paper is trying to address.
multimodal diagnostic dialogues
clinical case reports
evidence integration
diagnostic reasoning
evaluation framework
A Source-Grounded Framework for Constructing and Evaluating Progressive Multimodal Diagnostic Dialogues from Clinical Case Reports
🤖 AI Summary
本文提出一种基于源的框架，用于从病例报告中构建和评估渐进式多模态诊断对话，解决现有方法无法有效整合逐步临床证据的问题。
📝 Abstract
Clinical diagnosis requires progressive integration of patient history, physical examination, laboratory findings, medical images, and diagnostic-informative tests. However, most multimodal medical benchmarks evaluate fixed inputs or endpoint answers, while fully interactive diagnostic agents conflate evidence selection with evidence interpretation. We present a source-grounded framework to construct progressive multimodal diagnostic dialogues from case reports and an evaluation strategy for assessing MLLMs on final diagnosis, diagnostic reasoning, and image-finding interpretation. Evaluation on 24 internal medicine case reports showed that our framework can accurately convert case reports into reference dialogues, achieving a diagnosis F1 of 0.99 and a reasoning-quality score of 4.79 out of 5. Evaluation on two frontier MLLMs (o4-mini and Claude Haiku 4.5) achieved reasoning-quality scores of 2.75 and 2.50, respectively, with substantially lower diagnosis, reasoning, and image-finding F1 scores. The results demonstrate that fluent responses do not necessarily reflect evidence-grounded clinical reasoning and highlight the utility of the proposed framework for evaluating multimodal diagnostic reasoning.
Innovation
Methods, ideas, or system contributions that make the work stand out.
source-grounded framework
multimodal diagnostic dialogues
clinical reasoning evaluation
🔎 Similar Papers
No similar papers found.
