• Medientyp: E-Artikel
  • Titel: Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays
  • Beteiligte: Gaube, Susanne; Suresh, Harini; Raue, Martina; Lermer, Eva; Koch, Timo K.; Hudecek, Matthias F. C.; Ackery, Alun D.; Grover, Samir C.; Coughlin, Joseph F.; Frey, Dieter; Kitamura, Felipe C.; Ghassemi, Marzyeh; Colak, Errol
  • Erschienen: Springer Science and Business Media LLC, 2023
  • Erschienen in: Scientific Reports
  • Sprache: Englisch
  • DOI: 10.1038/s41598-023-28633-w
  • ISSN: 2045-2322
  • Schlagwörter: Multidisciplinary
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:title>Abstract</jats:title><jats:p>Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice’s quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants’ confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare.</jats:p>
  • Zugangsstatus: Freier Zugang