Validity and transferability of Model for ASsessing the value of Artificial Intelligence (MAS-AI).
Authors
Affiliations (8)
Affiliations (8)
- CIMT - Centre for Innovative Medical Technology, Odense University Hospital, Odense, Denmark; Program for Health System and Technology Evaluation, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada. Electronic address: [email protected].
- CIMT - Centre for Innovative Medical Technology, Odense University Hospital, Odense, Denmark.
- Program for Health System and Technology Evaluation, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada; Institute of Health Policy, Management & Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
- CAI-X - Centre for Clinical Artificial Intelligence, Odense University Hospital, Odense, Denmark.
- Department of Medicine and Surgery, Unit of Legal Medicine, University of Parma, Parma, Italy.
- Department of Biomedical, Metabolic and Neural Sciences, Unit of Legal Medicine, University of Modena and Reggio Emilia, Modena, Italy.
- Program for Health System and Technology Evaluation, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, Toronto General Hospital Research Institute, University Health Network, Toronto, Ontario, Canada; Institute of Health Policy, Management & Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
- CAI-X - Centre for Clinical Artificial Intelligence, Odense University Hospital, Odense, Denmark; Department of Radiology, Odense University Hospital, Odense, Denmark.
Abstract
In 2022, a multidisciplinary group of experts and patients published a Model for ASsessing the value of AI (MAS-AI) in medical imaging. MAS-AI is a critical tool for decision-makers, enabling them to make informed choices on the prioritization of AI solutions. The objective of this study was to assess the face validity and transferability of MAS-AI by investigating workshop participants' perceptions in Denmark, Italy, and Canada regarding the importance of its content. A Delphi process was conducted, including inputs from four workshops with a sample of decision makers from hospitals or the healthcare sector, patient partners and various researchers and experts. The participants were asked to rate the importance of each of the domains and subtopics in MAS-AI on a 0-3 Likert scale. A total of 95 participants from three countries participated. The face validity of all MAS-AI domains was confirmed by Denmark, Canada, and Italy, with over 70 percent of the respondents in the first round rating the domains as moderately or highly important. Overall, the five process factors were considered moderately or highly important by between 93 percent and 87 percent of the respondents. All the individual subtopics under each domain were rated above the 70 percent cut-off, except five subtopics for Italy. The study confirmed the validity of the MAS-AI domains in Denmark, Canada, and Italy. Several improvements in study design and data collection were identified. In the future, analyzing participants to understand which items were rated as important by whom could provide valuable insights.