目标：在韩国，放射学已被定位为早期采用基于人工智能的软件作为医疗设备(AI-SaMD)；然而，对当前的使用情况知之甚少，实施，以及AI-SaMD的未来需求。我们调查了韩国放射学会（KSR）成员对AI-SaMD的当前趋势和期望。
方法：一项匿名和自愿的在线调查在2023年4月17日至5月15日期间向所有KSR成员开放。调查的重点是使用AI-SaMD的经验，使用模式，满意度,以及对使用AI-SaMD的期望，包括行业的角色，政府,和KSR关于AI-SaMD的临床应用。
结果：在370名受访者中（回应率：7.7％[370/4792]；340名经董事会认证的放射科医师；210名来自学术机构），60.3%(223/370)有使用AI-SaMD的经验。受访者中AI-SaMD的两个最常见用例是病变检测(82.1%，183/223)，病变诊断/分类(55.2%，123/223），目标成像方式为平片(62.3%，139/223)，CT（42.6%，95/223)，乳房X线照相术(29.1%，65/223)，和MRI（28.7%，64/223)。大多数用户对AI-SaMD感到满意（67.6％[115/170，用于改善患者管理]至85.1％[189/222，用于性能]）。关于临床应用的扩展，大多数受访者表示倾向于AI-SaMD协助检测/诊断(77.0%，285/370），并进行自动测量/定量（63.5%，235/370）。大多数受访者表示，AI-SaMD的未来发展应侧重于提高实践效率(81.9%，303/370）和质量（71.4%，264/370)。总的来说,91.9％的受访者（340/370）同意需要KSR驱动的有关AI-SaMD使用的教育或指南。
结论：AI-SaMD在临床实践中的普及率和相应的满意度在KSR成员中很高。大多数AI-SaMD已用于病变检测，诊断,和分类。大多数受访者要求KSR驱动的教育或使用AI-SaMD的指南。
OBJECTIVE: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR).
METHODS: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs.
RESULTS: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs.
CONCLUSIONS: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.
