Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address these limitations due to its versatility in interpreting different data types and generating tailored outputs for diverse needs. Here, we describe BiomedGPT, the first open-source and lightweight vision-language foundation model, designed as a generalist capable of performing various biomedical tasks. BiomedGPT achieved state-of-the-art results in 16 out of 25 experiments while maintaining a computing-friendly model scale.
@article{zhang2024biomedgpt,title={A generalist vision--language foundation model for diverse biomedical tasks},author={Zhang, Kai and Zhou, Rong and Adhikarla, Eashan and Yan, Zhiling and Liu, Yixin and Yu, Jun and Liu, Zhengliang and Chen, Xun and Davison, Brian D and Ren, Hui and Huang, Jing and Chen, Chen and Zhou, Yuyin and Fu, Sunyang and Liu, Wei and Liu, Tianming and Li, Xiang and Chen, Yong and He, Lifang and Zou, James and Li, Quanzheng and Liu, Hongfang and Sun, Lichao},journal={Nature Medicine},pages={1--13},year={2024},publisher={Nature Publishing Group US},}