Department of Radiography, City, University of London, UK; Magnitiki Tomografia Kerkyras, Greece.
School of Health & Psychological Sciences, City, University of London, UK.
Int J Med Inform. 2024 Jun;186:105423. doi: 10.1016/j.ijmedinf.2024.105423. Epub 2024 Mar 25.
Medical Imaging and radiotherapy (MIRT) are at the forefront of artificial intelligence applications. The exponential increase of these applications has made governance frameworks necessary to uphold safe and effective clinical adoption. There is little information about how healthcare practitioners in MIRT in the UK use AI tools, their governance and associated challenges, opportunities and priorities for the future.
This cross-sectional survey was open from November to December 2022 to MIRT professionals who had knowledge or made use of AI tools, as an attempt to map out current policy and practice and to identify future needs. The survey was electronically distributed to the participants. Statistical analysis included descriptive statistics and inferential statistics on the SPSS statistical software. Content analysis was employed for the open-ended questions.
Among the 245 responses, the following were emphasised as central to AI adoption: governance frameworks, practitioner training, leadership, and teamwork within the AI ecosystem. Prior training was strongly correlated with increased knowledge about AI tools and frameworks. However, knowledge of related frameworks remained low, with different professionals showing different affinity to certain frameworks related to their respective roles. Common challenges and opportunities of AI adoption were also highlighted, with recommendations for future practice.
医学影像和放射治疗(MIRT)处于人工智能应用的前沿。这些应用的指数级增长使得有必要建立治理框架,以维护安全有效的临床应用。关于英国 MIRT 中的医疗保健从业者如何使用人工智能工具、他们的治理以及未来的相关挑战、机遇和重点,信息很少。
本横断面调查于 2022 年 11 月至 12 月向具有人工智能工具知识或使用经验的 MIRT 专业人员开放,旨在描绘当前的政策和实践,并确定未来的需求。调查通过电子方式分发给参与者。统计分析包括使用 SPSS 统计软件进行描述性统计和推断性统计。对开放式问题采用内容分析。
在 245 份回复中,以下内容被强调为人工智能采用的核心:治理框架、从业者培训、人工智能生态系统中的领导力和团队合作。先前的培训与对人工智能工具和框架的更多了解呈强相关。然而,相关框架的知识仍然很低,不同的专业人员根据其各自的角色对某些与框架表现出不同的亲和力。还强调了人工智能采用的常见挑战和机遇,并提出了未来实践的建议。