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

IF 7 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Investigative Radiology Pub Date : 2024-05-01 Epub Date: 2023-10-17 DOI:10.1097/RLI.0000000000001034
Boj Friedrich Hoppe, Johannes Rueckel, Yevgeniy Dikhtyar, Maurice Heimer, Nicola Fink, Bastian Oliver Sabel, Jens Ricke, Jan Rudolph, Clemens C Cyran
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Abstract

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.

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在急诊放射学中实施人工智能会影响医生的知识和感知:前瞻性前后分析。
目的:本研究的目的是评估将急诊放射学的人工智能(AI)解决方案纳入临床常规对医生感知和知识的影响。材料和方法:在2022年末,对用于射线照片骨折检测的AI算法实施前和实施后3个月进行了前瞻性介入调查。放射科医生和创伤科医生被问及他们对人工智能的知识和看法,Likert量表为7分(-3,“强烈反对”;+3,“强烈同意”)。自我生成的识别码允许在干预前和干预后匹配相同的个体,并对配对数据使用Wilcoxon符号秩检验。结果:共有47/71名匹配的参与者完成了这两项调查(66%的随访率),并有资格进行分析(34名放射科医生[72%],13名创伤科医生[28%],15名女性[32%];平均年龄34.8±7.8岁)。干预后,人工智能“减少了遗漏的发现”(1.28[前]vs 1.94[后],P=0.003),使读者“更安全”(1.21 vs 1.64,P=0.048),但没有“更快”(0.98 vs 1.21,P=0.0261)。人们越来越不同意人工智能可以“取代放射学报告”(-2.04 vs-2.34,P=0.038),以及关于“临床人工智能”、“机会”和“风险”的自我报告知识的增加(0.40对1.00、1.21对1.70和0.96对1.34;所有P≤0.028)。放射科医生比创伤科医生更频繁地使用人工智能结果(P<0.001),并将益处评为更高(所有P≤0.038),而资深医生不太可能使用人工智能或认可其益处(与年龄呈负相关,-0.35至0.30;所有P≤0.046)。结论:将人工智能用于急诊放射学纳入临床常规具有教育意义,并强调了人工智能作为“第二读者”的概念,以支持而不是取代医生。
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来源期刊
Investigative Radiology
Investigative Radiology 医学-核医学
CiteScore
15.10
自引率
16.40%
发文量
188
审稿时长
4-8 weeks
期刊介绍: Investigative Radiology publishes original, peer-reviewed reports on clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, and related modalities. Emphasis is on early and timely publication. Primarily research-oriented, the journal also includes a wide variety of features of interest to clinical radiologists.
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