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.

翻译：医学人工通用智能（AGI）是一个新兴领域，旨在开发专为医疗应用设计的系统，使其具备理解、学习并跨广泛任务与领域应用知识的能力。大语言模型（LLMs）代表着迈向AGI的重要一步。然而，在医学领域训练跨领域大语言模型面临显著挑战，这主要源于需要从不同领域收集数据。由于隐私限制和公开可用医学数据集的稀缺性，这一任务变得尤为困难。在此，我们提出医学AGI（MedAGI）——一种以最低成本统一领域特定医学大语言模型的范式，并建议了一条实现医学AGI的可行路径。随着医学领域中越来越多领域特定的专业多模态大语言模型被开发，MedAGI旨在通过我们新颖的自适应专家选择算法分析用户问题，从而自动选择合适的医学模型。它为现有医学领域的大语言模型提供了一种统一方法，无需因引入新模型而重新训练。这一特性使其成为动态发展的医学领域中一种面向未来的解决方案。为展示MedAGI的稳健性，我们针对三个不同医学领域进行了评估：皮肤病学诊断、X射线诊断以及病理图片分析。结果表明，MedAGI表现出卓越的通用性和可扩展性，在不同领域均展现出优异性能。我们的代码已在https://github.com/JoshuaChou2018/MedAGI上公开，以促进进一步研究。