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Artificial Intelligence for Emergency Care Triage—Much Promise, but Still Much to Learn

  • Published:2024-05-07 Issue:5 Volume:7 Page:e248857
  • ISSN:2574-3805
  • Container-title:JAMA Network Open
  • language:en
  • Short-container-title:JAMA Netw Open

Author:

Friedman Ari B.123,Delgado M. Kit1345,Weissman Gary E.3467

Affiliation:

1. Department of Emergency Medicine, University of Pennsylvania, Philadelphia

2. Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia

3. The Leonard Davis Institute, University of Pennsylvania, Philadelphia

4. Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia

5. Center for Health Care Transformation and Innovation, University of Pennsylvania, Philadelphia

6. Palliative and Advanced Illness Research Center, University of Pennsylvania Perelman School of Medicine, Philadelphia

7. Pulmonary, Allergy, and Critical Care Division, University of Pennsylvania Perelman School of Medicine, Philadelphia

Publisher

American Medical Association (AMA)

Reference6 articles.

1. Health system–scale language models are all-purpose prediction engines.;Jiang;Nature,2023

2. Use of a large language model to assess clinical acuity of adults in the emergency department.;Williams;JAMA Netw Open,2024

3. Automation bias in mammography: the impact of artificial intelligence BI-RADS suggestions on reader performance.;Dratsch;Radiology,2023

4. No place to call home—policies to reduce ED use in Medicaid.;Friedman;N Engl J Med,2015

5. Effectiveness of acute care remote triage systems: a systematic review.;Boggan;J Gen Intern Med,2020
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