Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest Cost

• URL: http://arxiv.org/abs/2306.10765v1
• Date: Mon, 19 Jun 2023 08:15:14 GMT
• Title: Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest Cost
• Authors: Juexiao Zhou, Xiuying Chen, Xin Gao
• Abstract summary: Medical artificial general intelligence (AGI) aims to develop systems that can understand, learn, and apply knowledge across a wide range of tasks and domains. Large language models (LLMs) represent a significant step towards AGI. We propose Medical AGI (MedAGI), a paradigm to unify domain-specific medical LLMs with the lowest cost.
• Score: 18.4295882376915
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Medical artificial general intelligence (AGI) is an emerging field that aims to develop systems specifically designed for medical applications that possess the ability to understand, learn, and apply knowledge across a wide range of tasks and domains. Large language models (LLMs) represent a significant step towards AGI. However, training cross-domain LLMs in the medical field poses significant challenges primarily attributed to the requirement of collecting data from diverse domains. This task becomes particularly difficult due to privacy restrictions and the scarcity of publicly available medical datasets. Here, we propose Medical AGI (MedAGI), a paradigm to unify domain-specific medical LLMs with the lowest cost, and suggest a possible path to achieve medical AGI. With an increasing number of domain-specific professional multimodal LLMs in the medical field being developed, MedAGI is designed to automatically select appropriate medical models by analyzing users' questions with our novel adaptive expert selection algorithm. It offers a unified approach to existing LLMs in the medical field, eliminating the need for retraining regardless of the introduction of new models. This characteristic renders it a future-proof solution in the dynamically advancing medical domain. To showcase the resilience of MedAGI, we conducted an evaluation across three distinct medical domains: dermatology diagnosis, X-ray diagnosis, and analysis of pathology pictures. The results demonstrated that MedAGI exhibited remarkable versatility and scalability, delivering exceptional performance across diverse domains. Our code is publicly available to facilitate further research at https://github.com/JoshuaChou2018/MedAGI.

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