RESUMEN
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.
Asunto(s)
Inteligencia Artificial , Radiología , Humanos , Flujo de Trabajo , Radiografía , Recolección de DatosRESUMEN
PURPOSE: We aimed to systematically analyse how the radiology community discusses the concept of artificial intelligence (AI), perceives its benefits, and reflects on its limitations. METHODS: We conducted a qualitative, systematic discourse analysis on 200 social-media posts collected over a period of five months (April-August 2020). RESULTS: The discourse on AI is active, albeit often referring to AI as an umbrella term and lacking precision on the context (e.g. research, clinical) and the temporal focus (e.g. current AI, future AI). The discourse is also somewhat split between optimism and pessimism. The latter considers a wider range of social, ethical and legal factors than the former, which tends to focus on concrete technologies and their functionalities. CONCLUSIONS: Further precision in the discourse could lead to more constructive conversations around AI. The split between optimism and pessimism calls for a constant exchange and synthesis between the two perspectives. Practical conversations (e.g. business models) remain rare, but may be crucial for an effective implementation of AI in clinical practice.