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aihc [2022/09/23 15:19]
nigam |
aihc [2023/11/09 10:11] (current)
nigam |
| ====== Stanford Medicine Program for Artificial Intelligence in Healthcare ====== | #REDIRECT :aihc |
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| In healthcare, predictive models play a role not unlike that of blood tests, X-rays, or MRIs: They influence decisions about whether an intervention is appropriate. Whether a model is usefulness depends on the interplay between the model's output, the intervention it triggers, and the intervention’s benefits and harms. We are working on a set of efforts collectively referred to as the [[https://stanfordhealthcare.org/stanford-health-now/ceo-report/advancing-precision-health-takes-real-smarts.html|Stanford Medicine Program for Artificial Intelligence in Healthcare]], with the mission of bringing AI technologies to the clinic, safely, cost-effectively and ethically. | |
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| {{::model-interplay.png?nolink&400|}} | |
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| Our research evolved from the [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.html|effort]] in improving palliative care using machine learning [[https://jamanetwork.com/journals/jama/fullarticle/2748179?guestAccessKey=8cef0271-616d-4e8e-852a-0fddaa0e5101|Ensuring that machine learning models are clinically useful]] requires [[https://www.nature.com/articles/s41591-019-0651-8| estimating the hidden deployment cost of predictive models]] as well as quantifying the [[http://academic.oup.com/jamia/article/28/6/1149/6045012|impact of work capacity constraints]] on achievable benefit, estimating [[https://www.sciencedirect.com/science/article/pii/S1532046421001544|individualized utility]], and learning [[https://pubmed.ncbi.nlm.nih.gov/34350942/|optimal decision thresholds]]. Pre-empting [[https://www.nejm.org/doi/full/10.1056/NEJMp1714229|ethical challenges]] often requires keeping [[https://hai.stanford.edu/news/when-algorithmic-fairness-fixes-fail-case-keeping-humans-loop|humans in the loop]]. | |
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| {{youtube>GNTIoEADfY4?small | Artificial Intelligence transforms health care}} | |
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| Russ Altman and Nigam Shah taking an in-depth look at the growing influence of “data-driven medicine.” | |
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| {{youtube>gQu2HbusrGQ?small&start=39 | Keeping the Human in the Loop for Equitable and Fair Use of ML in Healthcare}} | |
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| Keeping the Human in the Loop for Equitable and Fair Use of ML in Healthcare, at AIMiE 2018 | |
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| {{youtube>xW3drA3ijRc?small | Building a Machine Learning Healthcare System, at XLDB 2018}} | |
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| Building a Machine Learning Healthcare System, at XLDB, April 30 2018 | |
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