2023 Industry Perceptions Survey on AI Adoption and Return on Investment.

Mitchell Goldburgh, Michael LaChance, Julia Komissarchik, Julia Patriarche, Joe Chapa, Oliver Chen, Priya Deshpande, Matthew Geeslin, Julia Komissarchik, Nina Kottler, Julia Patriarche, Jennifer Sommer, Marcus Ayers, Vedrana Vujic
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Abstract

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.

2023 年人工智能应用和投资回报行业认知调查。
这份由 SIIM 赞助的 2023 年报告重点介绍了与诊断成像、生命科学和对比度有关的人工智能应用障碍和成功的行业观点。总体而言,我们的 2023 年调查表明,在多种用途中采用人工智能方面取得了进展,而且对人工智能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缩影。与 2021 年的调查相比,2023 年的受访者表示更广泛地采用了人工智能,但他们认为这还远远不够。具体来说,随着人们对人工智能在放射学中的投资回报来源有了更好的了解,供应商/客户的使用案例研究也证明了这一点。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、检测和特征描述方面。此外,还提到了用于提高报告效率的生成性人工智能。由于支付方的报销仍然遥遥无期,投资回报率的讨论扩展到了其他因素,包括医院手术和入院人数的增加、放射医师工作效率的提高以及综合医疗网络患者治疗效果的改善。在展望人工智能应用的长远前景时,受访者经常提到,人工智能在美国以外的地区有机会通过更复杂的人工智能和更易于管理/可见的投资回报率获得更广泛的应用。受访者关注的重点是人工智能和食品与药物管理局流程的信任障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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