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 this https URL

提出了 MedAGI 作为一个将领域特定的医学 LLM 统一起来的新兴范式，将自适应专家选择算法应用于解决由于缺少数据和保护隐私而导致的交叉培训挑战，并在皮肤科、X 光诊断和病理学图像分析领域进行了评估，结果表明 MedAGI 具有出色的通用性和可扩展性。