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
Erik H.M. Kemper (Erasmus MC)
Hendrik Erenstein (Hanze Hogeschool Groningen, University Medical Center Groningen)
Bart Jan Boverhof ( Erasmus Universiteit Rotterdam)
Ken Redekop ( Erasmus Universiteit Rotterdam)
Anna E. Andreychenko (K-SkAI LLC, ITMO University)
Matthias Dietzel (University Hospital Erlangen)
Kevin B.W. Groot Lipman (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)
Frans Vos (TU Delft - Applied Sciences, Erasmus MC, TU Delft - ImPhys/Computational Imaging)
Jacob J. Visser (Erasmus MC)
undefined More Authors
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.
help_outline