TY - JOUR
T1 - Model for ASsessing the value of Artificial Intelligence in medical imaging (MAS-AI)
AU - Fasterholdt, Iben
AU - Kjølhede, Tue
AU - Naghavi-Behzad, Mohammad
AU - Schmidt, Thomas
AU - Rautalammi, Quinnie T S
AU - Hildebrandt, Malene G
AU - Gerdes, Anne
AU - Barkler, Astrid
AU - Kidholm, Kristian
AU - Rac, Valeria E
AU - Rasmussen, Benjamin S B
PY - 2022/10/3
Y1 - 2022/10/3
N2 - 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.
AB - 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.
KW - Kunstig Intelligens
KW - Artificial Intelligence
KW - Delivery of Health Care
KW - Diagnostic Imaging
KW - Health Facilities
KW - Humans
KW - Technology Assessment, Biomedical
KW - evaluation
KW - Key words value assessment
KW - HTA
KW - artificial intelligence
KW - medical imaging
U2 - 10.1017/S0266462322000551
DO - 10.1017/S0266462322000551
M3 - Journal article
C2 - 36189821
VL - 38
JO - International Journal of Technology Assessment in Health Care
JF - International Journal of Technology Assessment in Health Care
SN - 0266-4623
IS - 1
M1 - e74
ER -