  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. However, existing biomedical generalist AI solutions are typically
heavyweight and closed source to researchers, practitioners, and patients.
Here, we propose 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. We also conducted human evaluations to assess the capabilities of
BiomedGPT in radiology visual question answering, report generation, and
summarization. BiomedGPT exhibits robust prediction ability with a low error
rate of 3.8% in question answering, satisfactory performance with an error rate
of 8.3% in writing complex radiology reports, and competitive summarization
ability with a nearly equivalent preference score to human experts. Our method
demonstrates that effective training with diverse data can lead to more
practical biomedical AI for improving diagnosis and workflow efficiency.
