Goldburgh Mitchell, LaChance Michael, Komissarchik Julia, Patriarche Julia, Chapa Joe, Chen Oliver, Deshpande Priya, Geeslin Matthew, Kottler Nina, Sommer Jennifer, Ayers Marcus, Vujic Vedrana
NTT DATA, Tokyo, Japan.
LaChance Executive Consulting, Washington, DC, USA.
J Imaging Inform Med. 2025 Apr;38(2):663-670. doi: 10.1007/s10278-024-01147-1. Epub 2024 Aug 20.
This SIIM-sponsored 2023 report highlights an industry view on artificial intelligence adoption barriers and success related to diagnostic imaging, life sciences, and contrasts. In general, our 2023 survey indicates that there has been progress in adopting AI across multiple uses, and there continues to be an optimistic forecast for the impact on workflow and clinical outcomes. This report, as in prior years, should be seen as a snapshot of the use of AI in imaging. Compared to our 2021 survey, the 2023 respondents expressed wider AI adoption but felt this was behind the potential. Specifically, the adoption has increased as sources of return on investment with AI in radiology are better understood as documented by vendor/client use case studies. Generally, the discussions of AI solutions centered on workflow triage, visualization, detection, and characterization. Generative AI was also mentioned for improving productivity in reporting. As payor reimbursement remains elusive, the ROI discussions expanded to look at other factors, including increased hospital procedures and admissions, enhanced radiologist productivity for practices, and improved patient outcomes for integrated health networks. When looking at the longer-term horizon for AI adoption, respondents frequently mentioned that the opportunity for AI to achieve greater adoption with more complex AI and a more manageable/visible ROI is outside the USA. Respondents focused on the barriers to trust in AI and the FDA processes.
这份由SIIM赞助的2023年报告突出了行业对人工智能在诊断成像、生命科学及造影方面的采用障碍和成功案例的看法。总体而言,我们2023年的调查表明,人工智能在多种用途上的采用已取得进展,并且对其对工作流程和临床结果的影响仍持乐观预测。与往年一样,本报告应被视为人工智能在成像领域应用的一个快照。与我们2021年的调查相比,2023年的受访者表示人工智能的采用范围更广,但认为这仍落后于其潜力。具体而言,随着供应商/客户用例研究记录的对放射学中人工智能投资回报来源的更好理解,采用率有所提高。一般来说,人工智能解决方案的讨论集中在工作流程分流、可视化、检测和特征描述上。生成式人工智能也被提及可提高报告效率。由于支付方报销仍难以实现,投资回报率的讨论扩展到考虑其他因素,包括医院手术和入院人数增加、提高放射科医生的工作效率以及改善综合医疗网络的患者治疗效果。展望人工智能采用的更长远前景时,受访者经常提到,人工智能在美国以外地区有机会通过更复杂的人工智能和更易于管理/可见的投资回报率实现更大范围的采用。受访者关注对人工智能信任的障碍以及美国食品药品监督管理局的流程。