Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States of America.
American College of Radiology, Reston, VA, United States of America.
Clin Imaging. 2024 Nov;115:110282. doi: 10.1016/j.clinimag.2024.110282. Epub 2024 Sep 10.
AI adoption requires perceived value by end-users. AI-enabled opportunistic CT screening (OS) detects incidental clinically meaningful imaging risk markers on CT for potential preventative health benefit. This investigation assesses radiologists' perspectives on AI and OS.
An online survey was distributed to 7500 practicing radiologists among ACR membership of which 4619 opened the emails. Familiarity with and views of AI applications were queried and tabulated, as well as knowledge of OS and inducements and impediments to use.
Respondent (n = 211) demographics: mean age 55 years, 73 % male, 91 % diagnostic radiologists, 46 % in private practice. 68 % reported using AI in practice, while 52 % were only somewhat familiar with AI. 70 % viewed AI positively though only 46 % reported AI's overall accuracy met expectations. 57 % were unfamiliar with OS, with 52 % of those familiar having a positive opinion. Patient perceptions were the most commonly reported (25 %) inducement for OS use. Provider (44 %) and patient (40 %) costs were the most common impediments. Respondents reported that osteoporosis/osteopenia (81 %), fatty liver (78 %), and atherosclerotic cardiovascular disease risk (76 %) could be well assessed by OS. Most indicated OS output requires radiologist oversight/signoff and should be included in a separate "screening" section in the Radiology report. 28 % indicated willingness to spend 1-3 min reviewing AI-generated output while 18 % would not spend any time. Society guidelines/recommendations were most likely to impact OS implementation.
Radiologists' perspectives on AI and OS provide practical insights on AI implementation. Increasing end-user familiarity with AI-enabled applications and development of society guidelines/recommendations are likely essential prerequisites for Radiology AI adoption.
人工智能的采用需要终端用户感知到其价值。人工智能辅助的机会性 CT 筛查（OS）可以在 CT 上检测到偶然的具有临床意义的影像学风险标志物，从而获得潜在的预防健康益处。本研究评估了放射科医生对人工智能和 OS 的看法。
向 ACR 成员中的 7500 名执业放射科医生发送了在线调查，其中 4619 人打开了电子邮件。调查询问并列出了他们对人工智能应用的熟悉程度和看法，以及对 OS 的了解程度、使用的诱因和障碍。
受访者（n=211）的人口统计学特征：平均年龄 55 岁，73%为男性，91%为诊断放射科医生，46%为私人执业医生。68%的人报告在实践中使用 AI，而 52%的人只是对 AI 有一定的了解。70%的人对 AI 持积极态度，尽管只有 46%的人报告 AI 的总体准确性符合预期。57%的人不熟悉 OS，熟悉 OS 的人中 52%的人持积极态度。患者的看法是 OS 使用最常见的诱因（25%）。提供者（44%）和患者（40%）的费用是最常见的障碍。受访者报告说，OS 可以很好地评估骨质疏松/骨量减少（81%）、脂肪肝（78%）和动脉粥样硬化性心血管疾病风险（76%）。大多数人表示 OS 的输出需要放射科医生的监督/签名，并应包含在放射学报告的单独“筛查”部分中。28%的人表示愿意花费 1-3 分钟来查看 AI 生成的输出，而 18%的人则不愿意花费任何时间。社会指南/建议最有可能影响 OS 的实施。
放射科医生对人工智能和 OS 的看法为人工智能的实施提供了实际的见解。提高终端用户对人工智能应用的熟悉程度以及制定社会指南/建议可能是放射科人工智能采用的必要前提。
