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. 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? A technography study in 2021

Mohammad H Rezazade Mehrizi  1 , Simon H Gerritsen  2 , Wouter M de Klerk  2 , Chantal Houtschild  2 , Silke M H Dinnessen  2 , Luna Zhao  2 , Rik van Sommeren  2 , Abby Zerfu  2
Affiliations

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

Mohammad H Rezazade Mehrizi et al. Eur Radiol. 2023 Feb.

Abstract

Objectives: How do providers of artificial intelligence (AI) solutions propose and legitimize the values of their solutions for supporting diagnostic radiology workflow?

Methods: 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.

Results: 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%).

Conclusions: 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.

Key points: • 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.

Keywords: Artificial intelligence; Legitimization; Radiology; Value proposition.

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Conflict of interest statement

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Figures

Fig. 1
Fig. 1
Characteristics of AI applications in radiology domain
Fig. 2
Fig. 2
The contribution of AI applications to radiology workflow
Fig. 3
Fig. 3
Characteristics of companies active in developing AI applications
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Fig. 4
Value propositions claimed by companies related to AI applications
Fig. 5
Fig. 5
Ways of legitimizing AI applications and their legal approvals
Fig. 6
Fig. 6
Different legitimization strategies used by companies providing AI applications
See this image and copyright information in PMC

References

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