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 ====== Stanford Medicine Program for Artificial Intelligence in Healthcare ====== ====== Stanford Medicine Program for Artificial Intelligence in Healthcare ======
  
-We are working on a set of efforts, which are 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. {{:​shc_program_for_ai_in_healthcare.pdf|See brochure}}. The four key components are:+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. {{:​shc_program_for_ai_in_healthcare.pdf|See brochure}}. The four key components are:
  
   - **Implementation**:​ We partner with the Technology and Digital Solutions team at Stanford Hospital to deploy predictive models in care delivery workflows. See our [[http://​stanmed.stanford.edu/​2018summer/​artificial-intelligence-puts-humanity-health-care.html|effort]] in improving palliative care and its [[https://​www.statnews.com/​2020/​07/​01/​end-of-life-artificial-intelligence/​|coverage in Statnews]].   - **Implementation**:​ We partner with the Technology and Digital Solutions team at Stanford Hospital to deploy predictive models in care delivery workflows. See our [[http://​stanmed.stanford.edu/​2018summer/​artificial-intelligence-puts-humanity-health-care.html|effort]] in improving palliative care and its [[https://​www.statnews.com/​2020/​07/​01/​end-of-life-artificial-intelligence/​|coverage in Statnews]].
   - **Ensuring that models are useful**: The utility of making a prediction and taking actions depends on factors beyond model accuracy, such as lead time offered by the prediction, the existence of a mitigating action, the cost and ease of intervening,​ the logistics of the intervention,​ and incentives of making the intervention.   - **Ensuring that models are useful**: The utility of making a prediction and taking actions depends on factors beyond model accuracy, such as lead time offered by the prediction, the existence of a mitigating action, the cost and ease of intervening,​ the logistics of the intervention,​ and incentives of making the intervention.
       - Check out our ideas on [[https://​jamanetwork.com/​journals/​jama/​fullarticle/​2748179?​guestAccessKey=8cef0271-616d-4e8e-852a-0fddaa0e5101|making machine learning models clinical useful]], [[https://​www.nature.com/​articles/​s41591-019-0651-8| estimating the hidden deployment cost of predictive models]], [[https://​innovations.bmj.com/​content/​6/​2/​45|bridging the implementation gap of machine learning in healthcare]],​ and developing a [[https://​www.nature.com/​articles/​s41746-020-00318-y|delivery science for AI in healthcare]].       - Check out our ideas on [[https://​jamanetwork.com/​journals/​jama/​fullarticle/​2748179?​guestAccessKey=8cef0271-616d-4e8e-852a-0fddaa0e5101|making machine learning models clinical useful]], [[https://​www.nature.com/​articles/​s41591-019-0651-8| estimating the hidden deployment cost of predictive models]], [[https://​innovations.bmj.com/​content/​6/​2/​45|bridging the implementation gap of machine learning in healthcare]],​ and developing a [[https://​www.nature.com/​articles/​s41746-020-00318-y|delivery science for AI in healthcare]].
-      - Read about our [[https://​academic.oup.com/​jamia/​article/​28/​6/​1149/​6045012|framework for quantifying the impact of healthcare delivery factors and work capacity constraints on achieved benefit]].+      - Read about our framework for quantifying the [[http://​academic.oup.com/​jamia/​article/​28/​6/​1149/​6045012|impact of work capacity constraints]] on achieved benefit, estimating [[https://​www.sciencedirect.com/​science/​article/​pii/​S1532046421001544|individualized utility]], and learning [[https://​pubmed.ncbi.nlm.nih.gov/​34350942/​|optimal decision thresholds]] from aggregate clinician behavior.
   - **Safety, ethics, and health system effects**: We map the multiple groups involved in taking action in response to a prediction and study their varying perspectives,​ positions, stakes, and commitments to pre-empt ethical challenges. Read our perspective on [[https://​www.nejm.org/​doi/​full/​10.1056/​NEJMp1714229|addressing ethical challenges]]. We believe that the use of AI can lead to good decisions if we keep [[https://​hai.stanford.edu/​news/​when-algorithmic-fairness-fixes-fail-case-keeping-humans-loop|human intelligence in the loop]].   - **Safety, ethics, and health system effects**: We map the multiple groups involved in taking action in response to a prediction and study their varying perspectives,​ positions, stakes, and commitments to pre-empt ethical challenges. Read our perspective on [[https://​www.nejm.org/​doi/​full/​10.1056/​NEJMp1714229|addressing ethical challenges]]. We believe that the use of AI can lead to good decisions if we keep [[https://​hai.stanford.edu/​news/​when-algorithmic-fairness-fixes-fail-case-keeping-humans-loop|human intelligence in the loop]].
   - **Training and Partnerships**:​ We partner with multiple groups to figure out “what would we do differently” if we had a prediction from a model and to investigate the pros and cons of using AI to guide care. For example, we [[https://​www.researchgate.net/​publication/​341829909_The_accuracy_vs_coverage_trade-off_in_patient-facing_diagnosis_models|examined the accuracy vs coverage trade off in patient facing diagnosis models]], and partnered with Google on efforts to enable [[https://​www.nature.com/​articles/​s41746-018-0029-1| scalable and accurate deep learning with electronic health records]]. Stanford students, check out the [[https://​stanfordmlgroup.github.io/​programs/​aihc-bootcamp/​|AI for Healthcare Bootcamp]].   - **Training and Partnerships**:​ We partner with multiple groups to figure out “what would we do differently” if we had a prediction from a model and to investigate the pros and cons of using AI to guide care. For example, we [[https://​www.researchgate.net/​publication/​341829909_The_accuracy_vs_coverage_trade-off_in_patient-facing_diagnosis_models|examined the accuracy vs coverage trade off in patient facing diagnosis models]], and partnered with Google on efforts to enable [[https://​www.nature.com/​articles/​s41746-018-0029-1| scalable and accurate deep learning with electronic health records]]. Stanford students, check out the [[https://​stanfordmlgroup.github.io/​programs/​aihc-bootcamp/​|AI for Healthcare Bootcamp]].
aihc.txt · Last modified: 2021/10/13 17:22 by nigam