How does artificial intelligence in radiology improve efficiency and health outcomes?

Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement.

van Leeuwen KG ，de Rooij M ，Schalekamp S ，van Ginneken B ，Rutten MJCM ... -  《-》

How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow? A technography study in 2021.

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.

Mehrizi MHR ，Gerritsen SH ，de Klerk WM ，Houtschild C ，Dinnessen SMH ，Zhao L ，van Sommeren R ，Zerfu A ... -  《-》

Artificial intelligence in radiology: 100 commercially available products and their scientific evidence.

van Leeuwen KG ，Schalekamp S ，Rutten MJCM ，van Ginneken B ，de Rooij M ... -  《-》

Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022.

To map the clinical use of CE-marked artificial intelligence (AI)-based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022. Our AI network (one radiologist or AI representative per Dutch hospital organization) received a questionnaire each spring from 2020 to 2022 about AI product usage, financing, and obstacles to adoption. Products that were not listed on www.AIforRadiology.com by July 2022 were excluded from the analysis. The number of respondents was 43 in 2020, 36 in 2021, and 33 in 2022. The number of departments using AI has been growing steadily (2020: 14, 2021: 19, 2022: 23). The diversity (2020: 7, 2021: 18, 2022: 34) and the number of total implementations (2020: 19, 2021: 38, 2022: 68) has rapidly increased. Seven implementations were discontinued in 2022. Four hospital organizations said to use an AI platform or marketplace for the deployment of AI solutions. AI is mostly used to support chest CT (17), neuro CT (17), and musculoskeletal radiograph (12) analysis. The budget for AI was reserved in 13 of the responding centers in both 2021 and 2022. The most important obstacles to the adoption of AI remained costs and IT integration. Of the respondents, 28% stated that the implemented AI products realized health improvement and 32% assumed both health improvement and cost savings. The adoption of AI products in radiology departments in the Netherlands is showing common signs of a developing market. The major obstacles to reaching widespread adoption are a lack of financial resources and IT integration difficulties. The clinical impact of AI starts with its adoption in daily clinical practice. Increased transparency around AI products being adopted, implementation obstacles, and impact may inspire increased collaboration and improved decision-making around the implementation and financing of AI products. • The adoption of artificial intelligence products for radiology has steadily increased since 2020 to at least a third of the centers using AI in clinical practice in the Netherlands in 2022. • The main areas in which artificial intelligence products are used are lung nodule detection on CT, aided stroke diagnosis, and bone age prediction. • The majority of respondents experienced added value (decreased costs and/or improved outcomes) from using artificial intelligence-based software; however, major obstacles to adoption remain the costs and IT-related difficulties.

van Leeuwen KG ，de Rooij M ，Schalekamp S ，van Ginneken B ，Rutten MJCM ... -  《-》

Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools.

In the past decade, there has been tremendous interest in applying artificial intelligence (AI) to improve the field of radiology. Currently, numerous AI applications are in development, with potential benefits spanning all steps of the imaging chain from test ordering to report communication. AI has been proposed as a means to optimize patient scheduling, improve worklist management, enhance image acquisition, and help radiologists interpret diagnostic studies. Although the potential for AI in radiology appears almost endless, the field is still in the early stages, with many uses still theoretical, in development, or limited to single institutions. Moreover, although the current use of AI in radiology has emphasized its clinical applications, some of which are in the distant future, it is increasingly clear that AI algorithms could also be used in the more immediate future for a variety of noninterpretive and quality improvement uses. Such uses include the integration of AI into electronic health record systems to reduce unwarranted variation in radiologists' follow-up recommendations and to improve other dimensions of radiology report quality. In the end, the potential of AI in radiology must be balanced with acknowledgment of its current limitations regarding generalizability and data privacy.

Kapoor N ，Lacson R ，Khorasani R 《-》