Liu Weiqi, Wu You, Zheng Zhuozhao, Yu Wei, Bittle Mark J, Kharrazi Hadi
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, United States.
Institute for Hospital Management, School of Medicine, Tsinghua University, Beijing, China.
J Clin Imaging Sci. 2024 Aug 23;14:31. doi: 10.25259/JCIS_72_2024. eCollection 2024.
This study assesses the perceptions and attitudes of Chinese radiologists concerning the application of artificial intelligence (AI) in the diagnosis of lung nodules.
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.
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).
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.
本研究评估中国放射科医生对人工智能(AI)在肺结节诊断中应用的看法和态度。
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包含26个问题的匿名问卷,这些问题涉及AI系统的可用性以及对AI技术的综合评估。数据收集于2023年7月19日至21日进行。
在90名受访者中,大多数人认可AI系统的便利性和可用性,这体现在系统可用性量表(SUS)的“良好”得分上(均值±标准差[SD]:74.3±11.9)。总体可用性也同样受到好评(均值±标准差:76.0±11.5),而可学习性被评为“可接受”(均值±标准差:67.5±26.4)。大多数放射科医生指出,使用AI系统后工作效率提高(李克特量表平均得分:4.6±0.6),诊断准确性提高(李克特量表平均得分:4.2±0.8)。对AI未来对放射科职业影响的看法各不相同(均值±标准差:3.2±1.4),但大家一致认为在可预见的未来AI不太可能完全取代放射科医生(均值±标准差:2.5±1.1)。
北京两家顶尖医院的放射科医生总体上对AI辅助肺结节诊断系统持积极看法,认为其用户友好且有效。然而,该系统的可学习性需要提高。虽然AI被认为有助于提高工作效率和诊断准确性,但其对职业的长期影响仍是一个有争议的话题。