GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI
This addresses the need for better evaluation tools for medical AI researchers and developers, though it is incremental as it builds on existing benchmark concepts with broader scope.
The authors tackled the lack of comprehensive benchmarks for evaluating Large Vision-Language Models (LVLMs) in medical applications by developing GMAI-MMBench, a benchmark with data from 284 datasets across 38 image modalities, 18 tasks, 18 departments, and 4 perceptual granularities. Results showed that even advanced models like GPT-4o achieved only 53.96% accuracy, highlighting significant room for improvement.
Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medical field, LVLMs have a high potential to offer substantial assistance for diagnosis and treatment. Before that, it is crucial to develop benchmarks to evaluate LVLMs' effectiveness in various medical applications. Current benchmarks are often built upon specific academic literature, mainly focusing on a single domain, and lacking varying perceptual granularities. Thus, they face specific challenges, including limited clinical relevance, incomplete evaluations, and insufficient guidance for interactive LVLMs. To address these limitations, we developed the GMAI-MMBench, the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format. Additionally, we implemented a lexical tree structure that allows users to customize evaluation tasks, accommodating various assessment needs and substantially supporting medical AI research and applications. We evaluated 50 LVLMs, and the results show that even the advanced GPT-4o only achieves an accuracy of 53.96%, indicating significant room for improvement. Moreover, we identified five key insufficiencies in current cutting-edge LVLMs that need to be addressed to advance the development of better medical applications. We believe that GMAI-MMBench will stimulate the community to build the next generation of LVLMs toward GMAI.