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Model for ASsessing the value of Artificial Intelligence in medical imaging (MAS-AI)

Part of: Special Collection - Methods

Published online by Cambridge University Press:  03 October 2022

Iben Fasterholdt Open the ORCID record for Iben Fasterholdt [Opens in a new window] ,
Tue Kjølhede ,
Mohammad Naghavi-Behzad ,
Thomas Schmidt ,
Quinnie T.S. Rautalammi ,
Malene G. Hildebrandt ,
Anne Gerdes ,
Astrid Barkler ,
Kristian Kidholm Open the ORCID record for Kristian Kidholm [Opens in a new window]  and
Valeria E. Rac
...Show all authors
Iben Fasterholdt*
Affiliation:
CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Odense, Denmark
Tue Kjølhede
Affiliation:
CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Odense, Denmark
Mohammad Naghavi-Behzad
Affiliation:
Department of Clinical Research, University of Southern Denmark, Odense, Denmark Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
Thomas Schmidt
Affiliation:
CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Odense, Denmark Health Informatics and Technology, University of Southern Denmark, Odense, Denmark
Quinnie T.S. Rautalammi
Affiliation:
Department of IT Management and Information Security, Region of Southern Denmark, Vejle, Denmark
Malene G. Hildebrandt
Affiliation:
CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Odense, Denmark Department of Clinical Research, University of Southern Denmark, Odense, Denmark Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
Anne Gerdes
Affiliation:
Department of Design and Communication, University of Southern Denmark, Kolding, Denmark
Astrid Barkler
Affiliation:
Patient representative, Odense University Hospital, Odense, Denmark
Kristian Kidholm
Affiliation:
CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Odense, Denmark
Valeria E. Rac
Affiliation:
Program for Health System and Technology Evaluation, Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
Benjamin S.B. Rasmussen
Affiliation:
Department of Radiology, Odense University Hospital, Odense, Denmark CAI-X – Centre for Clinical Artificial Intelligence, Odense University Hospital, Odense, Denmark
*
*Author for correspondence: Iben Fasterholdt, E-mail: [email protected]
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Abstract

Objectives

Artificial intelligence (AI) is seen as a major disrupting force in the future healthcare system. However, the assessment of the value of AI technologies is still unclear. Therefore, a multidisciplinary group of experts and patients developed a Model for ASsessing the value of AI (MAS-AI) in medical imaging. Medical imaging is chosen due to the maturity of AI in this area, ensuring a robust evidence-based model.

Methods

MAS-AI was developed in three phases. First, a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging. Next, we interviewed leading researchers in AI in Denmark. The third phase consisted of two workshops where decision makers, patient organizations, and researchers discussed crucial topics for evaluating AI. The multidisciplinary team revised the model between workshops according to comments.

Results

The MAS-AI guideline consists of two steps covering nine domains and five process factors supporting the assessment. Step 1 contains a description of patients, how the AI model was developed, and initial ethical and legal considerations. In step 2, a multidisciplinary assessment of outcomes of the AI application is done for the five remaining domains: safety, clinical aspects, economics, organizational aspects, and patient aspects.

Conclusions

We have developed an health technology assessment-based framework to support the introduction of AI technologies into healthcare in medical imaging. It is essential to ensure informed and valid decisions regarding the adoption of AI with a structured process and tool. MAS-AI can help support decision making and provide greater transparency for all parties.

Keywords

value assessmentHTAevaluationartificial intelligencemedical imaging
Type
Method
Information
International Journal of Technology Assessment in Health Care , Volume 38 , Issue 1 , 2022 , e74
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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