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Implementing Artificial Intelligence for Emergency Radiology Impacts Physicians' Knowledge and Perception: A Prospective Pre- and Post-Analysis.

التفاصيل البيبلوغرافية
العنوان: Implementing Artificial Intelligence for Emergency Radiology Impacts Physicians' Knowledge and Perception: A Prospective Pre- and Post-Analysis.
المؤلفون: Hoppe BF; From the Department of Radiology, University Hospital, LMU Munich, Munich, Germany (B.F.J., J.Rueckel, Y.D., M.H., N.F., B.O.S., J.Ricke, J.Rudolph, C.C.C.); and Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany (J.R.)., Rueckel J, Dikhtyar Y, Heimer M, Fink N, Sabel BO, Ricke J, Rudolph J, Cyran CC
المصدر: Investigative radiology [Invest Radiol] 2024 May 01; Vol. 59 (5), pp. 404-412. Date of Electronic Publication: 2023 Oct 17.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 0045377 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-0210 (Electronic) Linking ISSN: 00209996 NLM ISO Abbreviation: Invest Radiol Subsets: MEDLINE
أسماء مطبوعة: Publication: 1998- : Hagerstown, MD : Lippincott Williams & Wilkins
Original Publication: Philadelphia.
مواضيع طبية MeSH: Radiology* , Physicians*, Female ; Humans ; Adult ; Artificial Intelligence ; Prospective Studies ; Perception
مستخلص: Purpose: The aim of this study was to evaluate the impact of implementing an artificial intelligence (AI) solution for emergency radiology into clinical routine on physicians' perception and knowledge.
Materials and Methods: A prospective interventional survey was performed pre-implementation and 3 months post-implementation of an AI algorithm for fracture detection on radiographs in late 2022. Radiologists and traumatologists were asked about their knowledge and perception of AI on a 7-point Likert scale (-3, "strongly disagree"; +3, "strongly agree"). Self-generated identification codes allowed matching the same individuals pre-intervention and post-intervention, and using Wilcoxon signed rank test for paired data.
Results: A total of 47/71 matched participants completed both surveys (66% follow-up rate) and were eligible for analysis (34 radiologists [72%], 13 traumatologists [28%], 15 women [32%]; mean age, 34.8 ± 7.8 years). Postintervention, there was an increase that AI "reduced missed findings" (1.28 [pre] vs 1.94 [post], P = 0.003) and made readers "safer" (1.21 vs 1.64, P = 0.048), but not "faster" (0.98 vs 1.21, P = 0.261). There was a rising disagreement that AI could "replace the radiological report" (-2.04 vs -2.34, P = 0.038), as well as an increase in self-reported knowledge about "clinical AI," its "chances," and its "risks" (0.40 vs 1.00, 1.21 vs 1.70, and 0.96 vs 1.34; all P 's ≤ 0.028). Radiologists used AI results more frequently than traumatologists ( P < 0.001) and rated benefits higher (all P 's ≤ 0.038), whereas senior physicians were less likely to use AI or endorse its benefits (negative correlation with age, -0.35 to 0.30; all P 's ≤ 0.046).
Conclusions: Implementing AI for emergency radiology into clinical routine has an educative aspect and underlines the concept of AI as a "second reader," to support and not replace physicians.
Competing Interests: Conflicts of interest and sources of funding: none declared.
(Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)
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تواريخ الأحداث: Date Created: 20231016 Date Completed: 20240408 Latest Revision: 20240621
رمز التحديث: 20240622
DOI: 10.1097/RLI.0000000000001034
PMID: 37843828
قاعدة البيانات: MEDLINE
الوصف
تدمد:1536-0210
DOI:10.1097/RLI.0000000000001034