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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.07988 |
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| _version_ | 1866913886207213568 |
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| author | Zhou, Hong-Yu Acosta, Julián Nicolás Adithan, Subathra Datta, Suvrankar Topol, Eric J. Rajpurkar, Pranav |
| author_facet | Zhou, Hong-Yu Acosta, Julián Nicolás Adithan, Subathra Datta, Suvrankar Topol, Eric J. Rajpurkar, Pranav |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07988 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MedVersa: A Generalist Foundation Model for Medical Image Interpretation Zhou, Hong-Yu Acosta, Julián Nicolás Adithan, Subathra Datta, Suvrankar Topol, Eric J. Rajpurkar, Pranav Computer Vision and Pattern Recognition 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. |
| title | MedVersa: A Generalist Foundation Model for Medical Image Interpretation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.07988 |