评估人工智能在肺结节诊断中的作用:对中国两家试点三级医院放射科医生的调查。

IF 1.1 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Clinical Imaging Science Pub Date : 2024-08-23 eCollection Date: 2024-01-01 DOI:10.25259/JCIS_72_2024
Weiqi Liu, You Wu, Zhuozhao Zheng, Wei Yu, Mark J Bittle, Hadi Kharrazi
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引用次数: 0

摘要

研究目的本研究评估了中国放射科医生对人工智能(AI)应用于肺结节诊断的看法和态度:向北京安贞医院和北京清华长庚医院的所有放射科医生发放了一份匿名问卷,其中包括 26 个问题,涉及人工智能系统的可用性和人工智能技术的综合评价。数据收集工作于2023年7月19日至21日进行:结果:在 90 名受访者中,大多数人对人工智能系统的便利性和可用性表示满意,系统可用性量表(SUS)评分为 "良好"(平均值±标准差[SD]:74.3±11.9)。一般可用性同样受到好评(平均值±标准差:76.0 ± 11.5),而可学习性被评为 "可接受"(平均值±标准差:67.5 ± 26.4)。大多数放射科医生指出,人工智能系统提高了工作效率(平均李克特量表得分:4.6 ± 0.6)和诊断准确性(平均李克特量表得分:4.2 ± 0.8)。对于人工智能未来对放射学职业的影响,与会者看法不一(平均值±标准差:3.2±1.4),但一致认为人工智能在可预见的未来不太可能完全取代放射科医生(平均值±标准差:2.5±1.1):结论:北京两家知名医院的放射科医生普遍对人工智能辅助肺结节诊断系统持肯定态度,认为该系统使用方便、效果显著。然而,该系统的可学习性还需要加强。虽然人工智能被认为有利于提高工作效率和诊断准确性,但其对职业生涯的长期影响仍是一个争论的话题。
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Evaluating artificial intelligence's role in lung nodule diagnostics: A survey of radiologists in two pilot tertiary hospitals in China.

Objectives: This study assesses the perceptions and attitudes of Chinese radiologists concerning the application of artificial intelligence (AI) in the diagnosis of lung nodules.

Material and methods: An anonymous questionnaire, consisting of 26 questions addressing the usability of AI systems and comprehensive evaluation of AI technology, was distributed to all radiologists affiliated with Beijing Anzhen Hospital and Beijing Tsinghua Changgung Hospital. The data collection was conducted between July 19, and 21, 2023.

Results: Of the 90 respondents, the majority favored the AI system's convenience and usability, reflected in "good" system usability scale (SUS) scores (Mean ± standard deviation [SD]: 74.3 ± 11.9). General usability was similarly well-received (Mean ± SD: 76.0 ± 11.5), while learnability was rated as "acceptable" (Mean ± SD: 67.5 ± 26.4). Most radiologists noted increased work efficiency (Mean Likert scale score: 4.6 ± 0.6) and diagnostic accuracy (Mean Likert scale score: 4.2 ± 0.8) with the AI system. Views on AI's future impact on radiology careers varied (Mean ± SD: 3.2 ± 1.4), with a consensus that AI is unlikely to replace radiologists entirely in the foreseeable future (Mean ± SD: 2.5 ± 1.1).

Conclusion: Radiologists at two leading Beijing hospitals generally perceive the AI-assisted lung nodule diagnostic system positively, citing its user-friendliness and effectiveness. However, the system's learnability requires enhancement. While AI is seen as beneficial for work efficiency and diagnostic accuracy, its long-term career implications remain a topic of debate.

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来源期刊
Journal of Clinical Imaging Science
Journal of Clinical Imaging Science RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
2.00
自引率
0.00%
发文量
65
期刊介绍: The Journal of Clinical Imaging Science (JCIS) is an open access peer-reviewed journal committed to publishing high-quality articles in the field of Imaging Science. The journal aims to present Imaging Science and relevant clinical information in an understandable and useful format. The journal is owned and published by the Scientific Scholar. Audience Our audience includes Radiologists, Researchers, Clinicians, medical professionals and students. Review process JCIS has a highly rigorous peer-review process that makes sure that manuscripts are scientifically accurate, relevant, novel and important. Authors disclose all conflicts, affiliations and financial associations such that the published content is not biased.
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