Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study.

Journal: JAMA
PMID: 38112814

Abstract

IMPORTANCE: Artificial intelligence (AI) could support clinicians when diagnosing hospitalized patients; however, systematic bias in AI models could worsen clinician diagnostic accuracy. Recent regulatory guidance has called for AI models to include explanations to mitigate errors made by models, but the effectiveness of this strategy has not been established.

Authors

  • Sarah Jabbour
    Computer Science and Engineering, University of Michigan, Ann Arbor.
  • David Fouhey
    Computer Science and Engineering, University of Michigan, Ann Arbor.
  • Stephanie Shepard
    Computer Science and Engineering, University of Michigan, Ann Arbor.
  • Thomas S Valley
    Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, USA.
  • Ella A Kazerooni
    Departments of Radiology & Internal Medicine, University of Michigan Medical School, Michigan, MI, USA.
  • Nikola Banovic
    Computer Science and Engineering, University of Michigan, Ann Arbor.
  • Jenna Wiens
    Computer Science and Engineering, University of Michigan, Ann Arbor.
  • Michael W Sjoding
    1 Department of Internal Medicine, and.

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