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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: if@rsyd.dk
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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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References

Hashimoto, DA, Rosman, G, Rus, D, Meireles, OR. Artificial intelligence in surgery: Promises and perils. Ann Surg. 2018;268:70–76.CrossRefGoogle Scholar
Vaisman, A, Linder, N, Lundin, J, et al. Artificial intelligence, diagnostic imaging and neglected tropical diseases: Ethical implications. Bull World Health Organ. 2020;98:288–289.CrossRefGoogle ScholarPubMed
Winder, M, Owczarek, AJ, Chudek, J, Pilch-Kowalczyk, J, Baron, J. Are we overdoing it? Changes in diagnostic imaging workload during the years 2010–2020 including the impact of the SARS-CoV-2 pandemic. Healthcare (Basel). 2021;9:1557.CrossRefGoogle ScholarPubMed
Pesapane, F, Codari, M, Sardanelli, F. Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2:35.CrossRefGoogle ScholarPubMed
Challen, R, Denny, J, Pitt, M, Gompels, L, Edwards, T, Tsaneva-Atanasova, K. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019;28:231.CrossRefGoogle ScholarPubMed
Fasterholdt, I, Naghavi-Behzad, M, Rasmussen, B, et al. Value assessment of artificial intelligence in medical imaging: A scoping review. BMC Medical Imaging. 2022 (accepted for publication).Google Scholar
Cruz Rivera, S, Liu, X, Chan, A-W, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nat Med. 2020;26:1351–1363.CrossRefGoogle ScholarPubMed
FDA. Good machine learning practice for medical device development: Guiding principles. 2021. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles (accessed 22 September 2022).Google Scholar
Liu, X, Cruz Rivera, S, Moher, D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nat Med. 2020;26:1364–1374.CrossRefGoogle ScholarPubMed
Mongan, J, Moy, L, Kahn, CE Jr. Checklist for artificial intelligence in medical imaging (CLAIM): A guide for authors and reviewers. Radiol Artif Intell. 2020;2:e200029.CrossRefGoogle ScholarPubMed
Omoumi, P, Ducarouge, A, Tournier, A, et al. To buy or not to buy—Evaluating commercial AI solutions in radiology (the ECLAIR guidelines). Eur Radiol. 2021;31:3786–3796.CrossRefGoogle Scholar
Tsopra, R, Fernandez, X, Luchinat, C, et al. A framework for validating AI in precision medicine: Considerations from the European ITFoC consortium. BMC Med Inform Decis Mak. 2021;21:274.CrossRefGoogle ScholarPubMed
Haverinen, J, Keränen, N, Falkenbach, P, et al. Digi-HTA: Health technology assessment framework for digital healthcare services. Finnish J eHealth eWelfare. 2019;11:326–341.CrossRefGoogle Scholar
Kidholm, K, Ekeland, AG, Jensen, LK, et al. A model for assessment of telemedicine applications: Mast. Int J Technol Assess Health Care. 2012;28:44–51.CrossRefGoogle Scholar
Wild, C, Gartlehner, G. [Health technology assessment–evaluating health care interventions]. Wien Med Wochenschr. 2008;158:522–529.CrossRefGoogle Scholar
Kidholm, K, Clemensen, J, Caffery, LJ, Smith, AC. The model for assessment of telemedicine (MAST): A scoping review of empirical studies. J Telemed Telecare. 2017;23:803–813.CrossRefGoogle ScholarPubMed
Allner, R, Wilfling, D, Kidholm, K, Steinhäuser, J. Telemedizinprojekte im ländlichen Raum Deutschlands. Eine systematische Bewertung mit dem “Modell zur Evaluation von telemedizinischen Anwendungen”. Z Evidenz, Fortbildung Qual Gesundheitswesen. 2019;141-142:89–95.CrossRefGoogle Scholar
Ekeland, AG, Grøttland, A. Assessment of mast in European patient-centered telemedicine pilots. Int J Technol Assess Health Care. 2015;31:304–311.CrossRefGoogle ScholarPubMed
Kidholm, K, Jensen, LK, Kjølhede, T, Nielsen, E, Horup, MB. Validity of the model for assessment of telemedicine: A Delphi study. J Telemed Telecare. 2018;24:118–125.CrossRefGoogle ScholarPubMed
Fournaise, A, Lauridsen, JT, Bech, M, et al. Prevention of AcuTe admIssioN algorithm (PATINA): Study protocol of a stepped wedge randomized controlled trial. BMC Geriatr. 2021;21:146.CrossRefGoogle ScholarPubMed
EUnetHTA Joint Action 2. Work Package 8. HTA Core Model® version 3.0 (Pdf). 2016. Available from: www.htacoremodel.info/BrowseModel.aspx (accessed 22 September 2022).Google Scholar
Alami, H, Lehoux, P, Auclair, Y, et al. Artificial intelligence and health technology assessment: Anticipating a new level of complexity. J Med Internet Res. 2020;22:e17707.CrossRefGoogle ScholarPubMed
Krahn, M, Miller, F, Bayoumi, A, et al. Development of the Ontario decision framework: A values based framework for health technology assessment. Int J Technol Assess Health Care. 2018;34:290–299.CrossRefGoogle ScholarPubMed
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