Accelerating the integration of ChatGPT and other large‐scale AI models into biomedical research and healthcare - Wang, Ding‐Qiao; Feng, Long‐Yu; Ye, Jin‐Guo; Zou, Jin‐Gen; Zheng, Ying‐Feng (MedComm - Future medicine, 2023)
Large‐scale artificial intelligence (AI) models such as ChatGPT have the potential to improve performance on many benchmarks and real‐world tasks. However, it is difficult to develop and maintain these models because of their complexity and resource requirements. As a result, they are still inaccessible to healthcare industries and clinicians. This situation might soon be changed because of advancements in graphics processing unit (GPU) programming and parallel computing. More importantly, leveraging existing large‐scale AIs such as GPT‐4 and Med‐PaLM and integrating them into multiagent models (e.g., Visual‐ChatGPT) will facilitate real‐world implementations. This review aims to raise awareness of the potential applications of these models in healthcare. We provide a general overview of several advanced large‐scale AI models, including language models, vision‐language models, graph learning models, language‐conditioned multiagent models, and multimodal embodied models. We discuss their potential medical applications in addition to the challenges and future directions. Importantly, we stress the need to align these models with human values and goals, such as using reinforcement learning from human feedback, to ensure that they provide accurate and personalized insights that support human decision‐making and improve healthcare outcomes. This review provides an overview of large‐scale AI models, including language models (e.g., ChatGPT), vision‐language models, and language‐conditioned multiagent models, and discusses their potential applications in medicine, as well as their limitations and future trends. We also propose how large‐scale AI models can be integrated into various scenarios of clinical applications.