A Source-Grounded Framework for Constructing and Evaluating Progressive Multimodal Diagnostic Dialogues from Clinical Case Reports
- URL: http://arxiv.org/abs/2608.22713v1
- Date: Mon, 24 Aug 2026 02:04:39 GMT
- Title: A Source-Grounded Framework for Constructing and Evaluating Progressive Multimodal Diagnostic Dialogues from Clinical Case Reports
- Abstract summary: We present a source-grounded framework to construct progressive multimodal diagnostic dialogues from case reports.<n> Evaluation on 24 internal medicine case reports showed that our framework can accurately convert case reports into reference dialogues.<n>Results demonstrate that fluent responses do not necessarily reflect evidence-grounded clinical reasoning.
- Score: 19.75152371502983
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- MedReaMM: Evaluating Large Multimodal Models on Expert-Level Clinical Diagnostic Synthesis [61.266743965495884]
We introduce MedReaMM, a benchmark designed to evaluate models' ability to synthesize heterogeneous clinical evidence.<n>MedReaMM comprises 625 expert-validated cases with an average of 2.79 medical images per case and a total of 1,042 standardized diagnoses annotated with ICD-11 codes.<n>We evaluate 23 Large Multimodal Models (LMMs) and find that most achieve diagnostic accuracy scores below 50%, underscoring a substantial gap in multimodal diagnostic synthesis capability.
arXiv Detail & Related papers (2026-08-23T09:37:53Z) - Evaluating Multi-Turn Multimodal Diagnostic Reasoning on Challenging Real-World Clinical Cases [59.5328455571863]
ClinMM-Bench is the largest multi-turn multimodal clinical diagnostic evaluation benchmark to date.<n>It contains 1,089 challenging real-world clinical cases and 3,760 medical images across eight specialties.<n> proprietary models achieved the highest overall diagnostic accuracy, but the proportion of completely correct diagnoses remained limited.
arXiv Detail & Related papers (2026-07-28T16:19:03Z) - Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Support [20.0177310948999]
We introduce an OSCE-inspired standardized patient simulator and a controlled, reproducible benchmark for active diagnostic inquiry.<n>Across 468 cases and 15 models in our protocol, we observe that multi-turn evidence seeking reduces diagnostic accuracy by 12.75% and lowers supporting-evidence quality by 24.36%.
arXiv Detail & Related papers (2026-05-21T06:34:50Z) - MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning [66.94527468532843]
We propose MultiDx, a two-stage diagnostic reasoning framework.<n>It first generates suspected diagnoses and reasoning paths by leveraging knowledge from web search, SOAP-formatted case, and clinical case database.<n>Then it integrates multi-perspective evidence through matching, voting, and differential diagnosis to generate the final prediction.
arXiv Detail & Related papers (2026-04-27T08:46:29Z) - AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning [73.50200033931148]
We introduce AgentsEval, a multi-agent stream reasoning framework that emulates the collaborative diagnostic workflow of radiologists.<n>By dividing the evaluation process into interpretable steps including criteria definition, evidence extraction, alignment, and consistency scoring, AgentsEval provides explicit reasoning traces and structured clinical feedback.<n> Experimental results demonstrate that AgentsEval delivers clinically aligned, semantically faithful, and interpretable evaluations that remain robust under paraphrastic, semantic, and stylistic perturbations.
arXiv Detail & Related papers (2026-01-23T11:59:13Z) - PatientVLM Meets DocVLM: Pre-Consultation Dialogue Between Vision-Language Models for Efficient Diagnosis [4.118962495816488]
We propose a Pre-Consultation Dialogue Framework (PCDF) that mimics real-world diagnostic procedures.<n> Specifically, we simulate diagnostic dialogues between two vision-language models (VLMs): a DocVLM, which generates follow-up questions based on the image and dialogue history, and a PatientVLM, which responds using a symptom profile derived from the ground-truth diagnosis.
arXiv Detail & Related papers (2026-01-16T02:18:29Z) - Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in Large Language Models [51.91760712805404]
We introduce VivaBench, a benchmark for evaluating sequential clinical reasoning in large language models (LLMs)<n>Our dataset consists of 1762 physician-curated clinical vignettes structured as interactive scenarios that simulate a (oral) examination in medical training.<n>Our analysis identified several failure modes that mirror common cognitive errors in clinical practice.
arXiv Detail & Related papers (2025-10-11T16:24:35Z) - MedCaseReasoning: Evaluating and learning diagnostic reasoning from clinical case reports [49.00805568780791]
We introduce MedCaseReasoning, the first open-access dataset for evaluating Large Language Models (LLMs) on their ability to align with clinician-authored diagnostic reasoning.<n>The dataset includes 14,489 diagnostic question-and-answer cases, each paired with detailed reasoning statements.<n>We evaluate state-of-the-art reasoning LLMs on MedCaseReasoning and find significant shortcomings in their diagnoses and reasoning.
arXiv Detail & Related papers (2025-05-16T22:34:36Z) - Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias [5.421033429862095]
Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes.
This study explores the role of large language models in mitigating these biases through the utilization of a multi-agent framework.
arXiv Detail & Related papers (2024-01-26T01:35:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.