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aihc [2022/09/23 15:24] nigam |
aihc [2023/06/17 17:55] nigam |
====== Stanford Medicine Program for Artificial Intelligence in Healthcare ====== | ====== Program for Artificial Intelligence in Healthcare ====== |
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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. | 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&500 }} | {{ ::model-interplay.png?nolink |}} |
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Our research stemmed from the effort [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.html| 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]] and focus on examining the [[https://informatics.bmj.com/content/29/1/e100460 |consequences of model-guided decision making]] in the presence of clinical care guidelines. | Our research stemmed from the effort [[http://stanmed.stanford.edu/2018summer/artificial-intelligence-puts-humanity-health-care.html| 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]] and focus on examining the [[https://informatics.bmj.com/content/29/1/e100460 |consequences of model-guided decision making]] in the presence of clinical care guidelines. |