RareBench: Can LLMs Serve as Rare Diseases Specialists?

• URL: http://arxiv.org/abs/2402.06341v2
• Date: Thu, 4 Jul 2024 09:10:17 GMT
• Title: RareBench: Can LLMs Serve as Rare Diseases Specialists?
• Authors: Xuanzhong Chen, Xiaohao Mao, Qihan Guo, Lun Wang, Shuyang Zhang, Ting Chen,
• Abstract summary: Generalist Large Language Models (LLMs) have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates. RareBench is a pioneering benchmark designed to evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. We present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians.
• Score: 11.828142771893443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates primarily due to a lack of experienced physicians and the complexity of differentiating among many rare diseases. In this context, recent news such as "ChatGPT correctly diagnosed a 4-year-old's rare disease after 17 doctors failed" underscore LLMs' potential, yet underexplored, role in clinically diagnosing rare diseases. To bridge this research gap, we introduce RareBench, a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs' diagnostic performance. Moreover, we present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. This paves the way for exciting possibilities in future advancements in this field.

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