Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest
  Cost
Juexiao Zhou Xiuying Chen Xin Gao
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.