GPT-4V exhibits human-like performance in biomedical image classification

Abstract We demonstrate that GPT-4V(ision), a large multimodal model, exhibits strong one-shot learning ability, generalizability, and natural language interpretability in various biomedical image classification tasks, including classifying cell types, tissues, cell states, and disease status. Such features resemble human-like performance and distinguish GPT-4V from conventional image classification methods, which typically require large cohorts of training data and lack interpretability.