ESR Essentials: how to get to valuable radiology AI: the role of early health technology assessment-practice recommendations by the European Society of Medical Imaging Informatics

Abstract

AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large number of developed AI tools and those translated into daily clinical practice, which can be primarily attributed to limited usefulness and trust in current AI tools. Instead of technically driven development, little effort has been put into value-based development to ensure AI tools will have a clinically relevant impact on patient care. An iterative comprehensive value evaluation process covering the complete AI tool lifecycle should be part of radiology AI development. For value assessment of health technologies, health technology assessment (HTA) is an extensively used and comprehensive method. While most aspects of value covered by HTA apply to radiology AI, additional aspects, including transparency, explainability, and robustness, are unique to radiology AI and crucial in its value assessment. Additionally, value assessment should already be included early in the design stage to determine the potential impact and subsequent requirements of the AI tool. Such early assessment should be systematic, transparent, and practical to ensure all stakeholders and value aspects are considered. Hence, early value-based development by incorporating early HTA will lead to more valuable AI tools and thus facilitate translation to clinical practice. CLINICAL RELEVANCE STATEMENT: This paper advocates for the use of early value-based assessments. These assessments promote a comprehensive evaluation on how an AI tool in development can provide value in clinical practice and thus help improve the quality of these tools and the clinical process they support. KEY POINTS: Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains. Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools. Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.

Keywords: Artificial intelligence; Radiology; Stakeholder participation; Technology assessment (Biomedical); Value-based healthcare.

Fig. 1

Simplified overview of the radiology AI development. Four stages as distinguished by IJzerman et al [40]: Basic Research, Translational Research, Clinical Research, and Access and Pricing. Valuable AI in radiology can be determined by comprehensive value assessment such as the Health Technology Assessment (HTA) Core Model®. Guidance during the development process to achieve value and cover the development gap can be performed by an early Health Technology Assessment (eHTA) framework visualised here as an iterative process of HTA domain assessment and two eHTA methods (e.g. decision modelling and multiple criteria decision analysis). AI, artificial intelligence; eHTA, early Health Technology Assessment; HTA, Health Technology Assessment

Fig. 2

Overview of the Radiology AI Deployment and Assessment Rubric (RADAR) framework, which could form a basis for early health technology assessment in radiology AI. The outer circle depicts the RADAR efficacy level, and the inner circle provides its description. AI, artificial intelligence. Reproduced from [35] Boverhof BJ et al (2024). Licensed under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/