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rail [2024/02/18 20:05]
nigam [Responsible AI in Healthcare] |
rail [2024/03/17 15:34]
nigam [Creation and adoption of foundation models in medicine] |
| {{ :verify-benefits.png?400&nolink& }} | {{ :verify-benefits.png?400&nolink& }} |
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| We study whether commercial language models [[https://arxiv.org/abs/2304.13714 | support real-world needs]] or are able to follow [[https://medalign.stanford.edu/ |medical instructions]] that clinicians would expect them to follow. We build clinical foundation models such as [[https://www.sciencedirect.com/science/article/pii/S1532046420302653 | CLMBR]], [[https://arxiv.org/abs/2301.03150 | MOTOR]] and verify their benefits such as [[https://www.nature.com/articles/s41598-023-30820-8 | robustness over time]], [[https://pubmed.ncbi.nlm.nih.gov/37639620/ | populations]] and [[https://arxiv.org/abs/2311.11483 | sites]]. In addition we make available de-identified datasets such as [[https://ehrshot.stanford.edu/ | EHRSHOT]] for few-shot evaluation of foundation models and are working to release multi-modal datasets such as [[https://inspect.stanford.edu/ | INSPECT]]. | We study whether commercial language models [[https://arxiv.org/abs/2304.13714 | support real-world needs]] or can follow [[https://medalign.stanford.edu/ |medical instructions]] that clinicians would expect them to follow. We build clinical foundation models such as [[https://www.sciencedirect.com/science/article/pii/S1532046420302653 | CLMBR]], [[https://arxiv.org/abs/2301.03150 | MOTOR]] and verify their benefits such as [[https://www.nature.com/articles/s41598-023-30820-8 | robustness over time]], [[https://pubmed.ncbi.nlm.nih.gov/37639620/ | populations]] and [[https://arxiv.org/abs/2311.11483 | sites]]. we release de-identified datasets such as [[https://ehrshot.stanford.edu/ | EHRSHOT]] for few-shot evaluation of foundation models and multi-modal datasets such as [[https://inspect.stanford.edu/ | INSPECT]]. |
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| ===== Making machine learning models clinically useful ===== | ===== Making machine learning models clinically useful ===== |
