MedInsightBench: Evaluating Medical Analytics Agents Through Multi-Step Insight Discovery in Multimodal Medical Data

2025-12-15

AI Summary

Existing medical vision benchmarks inadequately evaluate large multimodal models’ (LMMs) capacity to discover multi-step, actionable clinical insights from medical imaging. To address this gap, we introduce MedInsightBench—the first benchmark specifically designed for multi-step medical insight discovery—comprising 332 expert-annotated clinical cases with hierarchical diagnostic reasoning and intervention-relevant ground truths. We further propose MedInsightAgent, a three-module agent framework integrating visual root-cause localization, analytical insight generation, dynamic question synthesis, and medical-knowledge-guided chain-of-reasoning. This architecture explicitly bridges the gaps in domain specificity and progressive reasoning inherent in general-purpose LMMs. Evaluated on MedInsightBench, MedInsightAgent significantly enhances the insight discovery performance of state-of-the-art LMMs across multiple dimensions—including diagnostic accuracy, clinical actionability, and reasoning fidelity—demonstrating the critical value of domain-specialized agents for diagnostic support and clinical decision assistance.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In medical data analysis, extracting deep insights from complex, multi-modal datasets is essential for improving patient care, increasing diagnostic accuracy, and optimizing healthcare operations. However, there is currently a lack of high-quality datasets specifically designed to evaluate the ability of large multi-modal models (LMMs) to discover medical insights. In this paper, we introduce MedInsightBench, the first benchmark that comprises 332 carefully curated medical cases, each annotated with thoughtfully designed insights. This benchmark is intended to evaluate the ability of LMMs and agent frameworks to analyze multi-modal medical image data, including posing relevant questions, interpreting complex findings, and synthesizing actionable insights and recommendations. Our analysis indicates that existing LMMs exhibit limited performance on MedInsightBench, which is primarily attributed to their challenges in extracting multi-step, deep insights and the absence of medical expertise. Therefore, we propose MedInsightAgent, an automated agent framework for medical data analysis, composed of three modules: Visual Root Finder, Analytical Insight Agent, and Follow-up Question Composer. Experiments on MedInsightBench highlight pervasive challenges and demonstrate that MedInsightAgent can improve the performance of general LMMs in medical data insight discovery.

Problem

• Lacks benchmark for evaluating medical insight discovery in multimodal data
• Existing models struggle with multi-step analysis and medical expertise
• Proposes automated agent framework to enhance medical data analysis

Innovation

• Introduces MedInsightBench benchmark for evaluating medical analytics agents
• Proposes MedInsightAgent framework with three specialized analysis modules
• Improves general LMM performance in multi-step medical insight discovery