TY - JOUR
T1 - Broadening the HTA of medical AI
T2 - A review of the literature to inform a tailored approach
AU - Boverhof, Bart Jan
AU - Redekop, W. Ken
AU - Visser, Jacob J.
AU - Uyl-de Groot, Carin A.
AU - Rutten-van Mölken, Maureen P.M.H.
N1 - Publisher Copyright:
© 2024 Fellowship of Postgraduate Medicine
PY - 2024/6
Y1 - 2024/6
N2 - Objectives: As current health technology assessment (HTA) frameworks do not provide specific guidance on the assessment of medical artificial intelligence (AI), this study aimed to propose a conceptual framework for a broad HTA of medical AI. Methods: A systematic literature review and a targeted search of policy documents was conducted to distill the relevant medical AI assessment elements. Three exemplary cases were selected to illustrate various elements: (1) An application supporting radiologists in stroke-care (2) A natural language processing application for clinical data abstraction (3) An ICU-discharge decision-making application. Results: A total of 31 policy documents and 9 academic publications were selected, from which a list of 29 issues was distilled. The issues were grouped by four focus areas: (1) Technology & Performance, (2) Human & Organizational, (3) Legal & Ethical and (4) Transparency & Usability. Each assessment element was extensively discussed in the test, and the elements clinical effectiveness, clinical workflow, workforce, interoperability, fairness and explainability were further highlighted through the exemplary cases. Conclusion: The current methodology of HTA requires extension to make it suitable for a broad evaluation of medical AI technologies. The 29-item assessment list that we propose needs a tailored approach for distinct types of medical AI, since the conceptualisation of the issues differs across applications.
AB - Objectives: As current health technology assessment (HTA) frameworks do not provide specific guidance on the assessment of medical artificial intelligence (AI), this study aimed to propose a conceptual framework for a broad HTA of medical AI. Methods: A systematic literature review and a targeted search of policy documents was conducted to distill the relevant medical AI assessment elements. Three exemplary cases were selected to illustrate various elements: (1) An application supporting radiologists in stroke-care (2) A natural language processing application for clinical data abstraction (3) An ICU-discharge decision-making application. Results: A total of 31 policy documents and 9 academic publications were selected, from which a list of 29 issues was distilled. The issues were grouped by four focus areas: (1) Technology & Performance, (2) Human & Organizational, (3) Legal & Ethical and (4) Transparency & Usability. Each assessment element was extensively discussed in the test, and the elements clinical effectiveness, clinical workflow, workforce, interoperability, fairness and explainability were further highlighted through the exemplary cases. Conclusion: The current methodology of HTA requires extension to make it suitable for a broad evaluation of medical AI technologies. The 29-item assessment list that we propose needs a tailored approach for distinct types of medical AI, since the conceptualisation of the issues differs across applications.
UR - http://www.scopus.com/inward/record.url?scp=85192334981&partnerID=8YFLogxK
U2 - 10.1016/j.hlpt.2024.100868
DO - 10.1016/j.hlpt.2024.100868
M3 - Review article
AN - SCOPUS:85192334981
SN - 2211-8837
VL - 13
JO - Health Policy and Technology
JF - Health Policy and Technology
IS - 2
M1 - 100868
ER -
