Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases

The latest reasoning-enhanced large language models (reasoning LLMs), such as DeepSeek-R1 and OpenAI-o3, have demonstrated remarkable success. However, the application of such reasoning enhancements to the highly professional medical domain has not been clearly evaluated, particularly regarding with not only assessing the final generation but also examining the quality of their reasoning processes. In this study, we present MedR-Bench, a reasoning-focused medical evaluation benchmark comprising 1,453 structured patient cases with reasoning references mined from case reports. Our benchmark spans 13 body systems and 10 specialty disorders, encompassing both common and rare diseases. In our evaluation, we introduce a versatile framework consisting of three critical clinical stages: assessment recommendation, diagnostic decision-making, and treatment planning, comprehensively capturing the LLMs' performance across the entire patient journey in healthcare. For metrics, we propose a novel agentic system, Reasoning Evaluator, designed to automate and objectively quantify free-text reasoning responses in a scalable manner from the perspectives of efficiency, factuality, and completeness by dynamically searching and performing cross-referencing checks. As a result, we assess five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and others. Our results reveal that current LLMs can handle relatively simple diagnostic tasks with sufficient critical assessment results, achieving accuracy generally over 85%. However, they still struggle with more complex tasks, such as assessment recommendation and treatment planning. In reasoning, their reasoning processes are generally reliable, with factuality scores exceeding 90%, though they often omit critical reasoning steps. Our study clearly reveals further development directions for current clinical LLMs.

RaTEScore: A Metric for Radiology Report Generation

This paper introduces a novel, entity-aware metric, termed as Radiological Report (Text) Evaluation (RaTEScore), to assess the quality of medical reports generated by AI models. RaTEScore emphasizes crucial medical entities such as diagnostic outcomes and anatomical details, and is robust against complex medical synonyms and sensitive to negation expressions. Technically, we developed a comprehensive medical NER dataset, RaTE-NER, and trained an NER model specifically for this purpose. This model enables the decomposition of complex radiological reports into constituent medical entities. The metric itself is derived by comparing the similarity of entity embeddings, obtained from a language model, based on their types and relevance to clinical significance. Our evaluations demonstrate that RaTEScore aligns more closely with human preference than existing metrics, validated both on established public benchmarks and our newly proposed RaTE-Eval benchmark.

Large-scale Long-tailed Disease Diagnosis on Radiology Images

In this study, we aim to investigate the problem of large-scale, large-vocabulary disease classification for radiologic images, which can be formulated as a multi-modal, multi-anatomy, multi-label, long-tailed classification. Our main contributions are three folds: (i), on dataset construction, we build up an academically accessible, large-scale diagnostic dataset that encompasses 5568 disorders linked with 930 unique ICD-10-CM codes, containing 39,026 cases (192,675 scans). (ii), on model design, we present a novel architecture that enables to process arbitrary number of input scans, from various imaging modalities, which is trained with knowledge enhancement to leverage the rich domain knowledge; (iii), on evaluation, we initialize a new benchmark for multi-modal multi-anatomy long-tailed diagnosis. Our method shows superior results on it. Additionally, our final model serves as a pre-trained model, and can be finetuned to benefit diagnosis on various external datasets.

Can GPT-4V Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis

Driven by the large foundation models, the development of artificial intelligence has witnessed tremendous progress lately, leading to a surge of general interest from the public. In this study, we aim to assess the performance of OpenAI's newest model, GPT-4V(ision), specifically in the realm of multimodal medical diagnosis. Our evaluation encompasses 17 human body systems, including Central Nervous System, Head and Neck, Cardiac, Chest, Hematology, Hepatobiliary, Gastrointestinal, Urogenital, Gynecology, Obstetrics, Breast, Musculoskeletal, Spine, Vascular, Oncology, Trauma, Pediatrics, with images taken from 8 modalities used in daily clinic routine, e.g., X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Digital Subtraction Angiography (DSA), Mammography, Ultrasound, and Pathology. We probe the GPT-4V's ability on multiple clinical tasks with or without patent history provided, including imaging modality and anatomy recognition, disease diagnosis, report generation, disease localisation. Our observation shows that, while GPT-4V demonstrates proficiency in distinguishing between medical image modalities and anatomy, it faces significant challenges in disease diagnosis and generating comprehensive reports. These findings underscore that while large multimodal models have made significant advancements in computer vision and natural language processing, it remains far from being used to effectively support real-world medical applications and clinical decision-making. All images used in this report can be found in https://github.com/chaoyi-wu/GPT-4V_Medical_Evaluation.