Reflections on co-creating a model for the value assessment of artificial intelligence technologies.

AIMS: A multidisciplinary group of experts and patients developed the Model for ASsessing the value of Artificial Intelligence (MAS-AI) to ensure an evidence-based and patient-centered approach to introducing artificial intelligence technologies in healthcare. In this article, we share our experiences with meaningfully involving a patient in co-creating a research project concerning complex and technically advanced topics. METHODS: The co-creation was evaluated by means of initial reflections from the research team before the project started, in a continuous logbook, and through semi-structured interviews with patients and two researchers before and after the active co-creation phase of the project. RESULTS: There were initial doubts about the feasibility of including patients in this type of project. Co-creation ensured relevance to patients, a holistic research approach and the debate of ethical considerations. Due to one patient dropping out, it is important to foresee and support the experienced challenges of time and energy spent by the patient in future projects. Having a multidisciplinary team helped the collaboration. A mutual reflective evaluation provided insights into the process which we would otherwise have missed. CONCLUSIONS: We found it possible to create complex and data-intense research projects with patients. Including patients benefitted the project and gave researchers new perspectives on their own research. Mutual reflection throughout the project is key to maximise learning for all parties involved.

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

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.