MedVersa: A Generalist Foundation Model for Medical Image Interpretation
This addresses the limitation of narrow applications in medical AI for widespread clinical adoption, representing a significant advance rather than an incremental improvement.
The paper tackles the problem of narrow medical AI systems by introducing MedVersa, a generalist foundation model that achieves state-of-the-art performance in nine tasks, sometimes outperforming specialized solutions by over 10%, and matches or exceeds human reports in 71% of cases overall.
Current medical AI systems are often limited to narrow applications, hindering widespread adoption. We present MedVersa, a generalist foundation model trained on tens of millions of compiled medical instances. MedVersa unlocks generalist learning from multimodal inputs and outputs, representing the first example of a generalist model reaching competitive performance with leading specialized solutions across a variety of medical imaging scenarios. MedVersa achieves state-of-the-art performance in nine tasks, sometimes outperforming counterparts by over 10%. Radiologist evaluation shows MedVersa-generated reports get superior performance in 95% of normal studies, while matching or exceeding human reports in 71% of cases overall. User studies showed notable reductions in report writing time and discrepancies with the use of MedVersa. Our findings underscore the value of flexible, multimodal AI systems in advancing medical image interpretation and supporting clinical expertise.