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aihc [2022/04/07 15:50]
nigam
aihc [2022/04/07 15:54]
nigam
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       - Read about our views on the need for [[https://www.statnews.com/2022/03/17/health-related-ai-needs-rigorous-evaluation-and-guardrails/ | rigorous evaluation and guardrails]] when creating and using clinical AI tools, and how [[https://hai.stanford.edu/news/should-ai-models-be-explainable-depends|explainability is overrated]].       - Read about our views on the need for [[https://www.statnews.com/2022/03/17/health-related-ai-needs-rigorous-evaluation-and-guardrails/ | rigorous evaluation and guardrails]] when creating and using clinical AI tools, and how [[https://hai.stanford.edu/news/should-ai-models-be-explainable-depends|explainability is overrated]].
       - 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 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.+      - Read about our [[https://analytics-dashboard.shinyapps.io/dashboard/ | dashboard]] 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]].
-If done right, the adoption of efficacious prediction-action pairs can massively improve the ability of a health system to find patients at risk and act early. As part of this effort, we search for clinical situations where the application of risk-stratification and proactive action can provide cost-effective, health system level benefits.+ 
 + 
 +We believe that the adoption of efficacious prediction-action pairing can massively improve the ability of a health system to find patients at risk and act early. 
  
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aihc.txt · Last modified: 2022/07/08 11:22 by nigam