KIN Center for Digital Innovation, School of Business and Economics, Vrije Universiteit Amsterdam, De Boelelaan 1105, VU Main Building A-Wing, 5th Floor, 1081, HV Amsterdam, The Netherlands.
Digital Business and Innovation, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Eur Radiol. 2023 Feb;33(2):915-924. doi: 10.1007/s00330-022-09090-x. Epub 2022 Aug 18.
How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow?
We systematically analyze 393 AI applications developed for supporting diagnostic radiology workflow. We collected qualitative and quantitative data by analyzing around 1250 pages of documents retrieved from companies' websites and legal documents. Five investigators read and interpreted collected data, extracted the features and functionalities of the AI applications, and finally entered them into an excel file for identifying the patterns.
Over the last 2 years, we see an increase in the number of AI applications (43%) and number of companies offering them (34%), as well as their average age (45%). Companies claim various value propositions related to increasing the "efficiency" of radiology work (18%)-e.g., via reducing the time and cost of performing tasks and reducing the work pressure-and "quality" of offering medical services (31%)-e.g., via enhancing the quality of clinical decisions and enhancing the quality of patient care, or both of them (28%). To legitimize and support their value propositions, the companies use multiple strategies simultaneously, particularly by seeking legal approvals (72%), promoting their partnership with medical and academic institutions (75%), highlighting the expertise of their teams (56%), and showcasing examples of implementing their solutions in practice (53%).
Although providers of AI applications claim a wide range of value propositions, they often provide limited evidence to show how their solutions deliver such systematic values in clinical practice.
• AI applications in radiology continue to grow in number and diversity. • Companies offering AI applications claim various value propositions and use multiple ways to legitimize these propositions. • Systematic scientific evidence showing the actual effectiveness of AI applications in clinical context is limited.
人工智能（AI）解决方案的提供者如何提出和证明其解决方案支持诊断放射学工作流程的价值观？
我们系统地分析了 393 种用于支持诊断放射学工作流程的 AI 应用程序。我们通过分析从公司网站和法律文件中检索到的约 1250 页文档，收集了定性和定量数据。五名研究人员阅读并解释了收集到的数据，提取了 AI 应用程序的功能和特性，最后将其输入电子表格以识别模式。
在过去的 2 年中，我们看到 AI 应用程序的数量（增长了 43%）和提供这些应用程序的公司数量（增长了 34%）以及它们的平均年龄（增长了 45%）都有所增加。公司声称有各种与提高放射科工作“效率”（18%）相关的价值主张，例如通过减少执行任务的时间和成本以及降低工作压力，以及提高提供医疗服务的“质量”（31%），例如通过提高临床决策的质量和提高患者护理的质量，或两者兼而有之（28%）。为了证明和支持他们的价值主张，公司同时使用多种策略，特别是通过寻求法律批准（72%）、宣传他们与医疗和学术机构的合作关系（75%）、强调他们团队的专业知识（56%）以及展示他们的解决方案在实践中的实施示例（53%）。
尽管 AI 应用程序的提供者声称有广泛的价值主张，但他们往往提供有限的证据来证明他们的解决方案如何在临床实践中提供这种系统性的价值。
放射科的 AI 应用程序数量继续增长，种类也越来越多。
提供 AI 应用程序的公司声称有各种价值主张，并使用多种方式来证明这些主张的合法性。
显示 AI 应用程序在临床环境中实际效果的系统科学证据有限。
