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rail [2024/04/30 16:20]
nigam [Creation and adoption of foundation models in medicine] |
rail [2024/05/12 10:55] (current)
nigam |
| ====== Responsible AI in Healthcare ====== | |
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| Our team is focused on bringing AI into clinical use, safely, ethically and cost effectively. Our work is organized in two broad work-streams. | |
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| ====== Responsible AI in Healthcare ====== | ====== Responsible AI in Healthcare ====== |
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| {{ :verify-benefits.png?nolink&400 }} | {{ :verify-benefits.png?nolink&400 }} |
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| 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]]. | 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 (MedAlign)]] 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 ===== |
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| Whether a classifier or prediction [[ https://jamanetwork.com/journals/jama/article-abstract/2748179 | model is usefulness]] in guiding care depends on the interplay between the model's output, the intervention it triggers, and the intervention’s benefits and harms. Our work stemmed from the effort [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.html|in improving palliative care]] using machine learning. [[https://www.tinyurl.com/hai-blogs | Blog posts at HAI]] summarize our work in easily accessible manner. | Whether a classifier or prediction [[ https://jamanetwork.com/journals/jama/article-abstract/2748179 | model is useful]] in guiding care depends on the interplay between the model's output, the intervention it triggers, and the intervention’s benefits and harms. Our work stemmed from the effort [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.html|in improving palliative care]] using machine learning. [[https://www.tinyurl.com/hai-blogs | Blog posts at HAI]] summarize our work in easily accessible manner. |
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| {{ :model-interplay.png?400&nolink& }} | {{ :model-interplay.png?400&nolink& }} |
