背景:人工智能(AI)的使用可以彻底改变医疗保健,但这引发了风险担忧。因此,了解临床医生如何信任和接受AI技术至关重要。胃肠病学,由于其性质是基于图像和干预重的专业,是人工智能辅助诊断和管理可以广泛应用的领域。
目的:本研究旨在研究胃肠病学家或胃肠外科医生如何接受和信任AI在计算机辅助检测(CADe)中的使用,计算机辅助表征(CADx),和计算机辅助干预(CADi)在结肠镜检查中结直肠息肉。
方法:我们于2022年11月至2023年1月进行了基于网络的问卷调查,涉及亚太地区的5个国家或地区。问卷包括用户背景和人口统计等变量;使用人工智能的意图,感知风险;接受;以及对人工智能辅助检测的信任,表征,和干预。我们为参与者提供了与结肠镜检查和结直肠息肉管理相关的3种AI方案。这些场景反映了结肠镜检查中现有的AI应用,即息肉的检测(CADe),息肉(CADx)的表征,和AI辅助息肉切除术(CADi)。
结果:总计,165胃肠病学家和胃肠外科医师使用医学交流专家设计的结构化问卷对基于网络的调查做出了回应。参与者的平均年龄为44岁(SD9.65),大部分为男性(n=116,70.3%),大多在公立医院工作(n=110,66.67%)。参与者报告了相对较高的AI暴露,111人(67.27%)报告使用人工智能进行消化系统疾病的临床诊断或治疗。胃肠病学家对在诊断中使用AI非常感兴趣,但在风险预测和接受AI方面表现出不同程度的保留。大多数参与者(n=112,72.72%)也表示有兴趣在未来的实践中使用AI。CADe被83.03%(n=137)的受访者接受,CADx被78.79%(n=130)接受,CADi的接受率为72.12%(n=119)。85.45%(n=141)的受访者信任CADe和CADx,72.12%(n=119)的受访者信任CADi。在风险认知方面没有特定应用的差异,但更有经验的临床医生给出了较低的风险评级.
结论:胃肠病学家报告了在大肠息肉治疗中使用AI辅助结肠镜检查的总体接受度和信任度较高。然而,此信任级别取决于应用场景。此外,风险感知之间的关系,接受,信任在胃肠病学实践中使用人工智能并不简单。
BACKGROUND: The use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by its nature of being an image-based and intervention-heavy specialty, is an area where AI-assisted diagnosis and management can be applied extensively.
OBJECTIVE: This study aimed to study how gastroenterologists or gastrointestinal surgeons accept and trust the use of AI in computer-aided detection (CADe), computer-aided characterization (CADx), and computer-aided intervention (CADi) of colorectal polyps in colonoscopy.
METHODS: We conducted a web-based questionnaire from November 2022 to January 2023, involving 5 countries or areas in the Asia-Pacific region. The questionnaire included variables such as background and demography of users; intention to use AI, perceived risk; acceptance; and trust in AI-assisted detection, characterization, and intervention. We presented participants with 3 AI scenarios related to colonoscopy and the management of colorectal polyps. These scenarios reflect existing AI applications in colonoscopy, namely the detection of polyps (CADe), characterization of polyps (CADx), and AI-assisted polypectomy (CADi).
RESULTS: In total, 165 gastroenterologists and gastrointestinal surgeons responded to a web-based survey using the structured questionnaire designed by experts in medical communications. Participants had a mean age of 44 (SD 9.65) years, were mostly male (n=116, 70.3%), and mostly worked in publicly funded hospitals (n=110, 66.67%). Participants reported relatively high exposure to AI, with 111 (67.27%) reporting having used AI for clinical diagnosis or treatment of digestive diseases. Gastroenterologists are highly interested to use AI in diagnosis but show different levels of reservations in risk prediction and acceptance of AI. Most participants (n=112, 72.72%) also expressed interest to use AI in their future practice. CADe was accepted by 83.03% (n=137) of respondents, CADx was accepted by 78.79% (n=130), and CADi was accepted by 72.12% (n=119). CADe and CADx were trusted by 85.45% (n=141) of respondents and CADi was trusted by 72.12% (n=119). There were no application-specific differences in risk perceptions, but more experienced clinicians gave lesser risk ratings.
CONCLUSIONS: Gastroenterologists reported overall high acceptance and trust levels of using AI-assisted colonoscopy in the management of colorectal polyps. However, this level of trust depends on the application scenario. Moreover, the relationship among risk perception, acceptance, and trust in using AI in gastroenterology practice is not straightforward.