Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, assessment of the value of these new technologies is still unclear and no agreed international HTA-based guideline exists. Therefore, a Model for ASsessing the value of AI (MAS-AI) in medical imaging was developed by a multidisciplinary group of experts and patient representatives.

The MAS-AI guideline is based on four steps. First a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging (5,890 studies were assessed with 86 studies included in the scoping review). Next, interviews with leading researchers in AI in Denmark. The third step was two workshops where decision-makers, patient organizations and researchers discussed crucial topics when evaluating AI. Between workshops, the multidisciplinary team revised the model according to comments from workshop-participants. Last step is a validation workshop in Canada.

The MAS-AI guideline has three parts. There are two steps covering nine domains and then advises for the evaluation process. Step 1 contains a description of patients, how the AI-model was developed, and initial ethical and legal considerations. Finishing the four domains in Step 1 is a prerequisite for moving to step 2. 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. The last part, is five advices to facilitate a good evaluation process.

We have developed an HTA based framework to support the prospective phase while introducing novel AI technologies into healthcare in medical imaging. MAS-AI can assist HTA organizations (and companies) in selecting the relevant domains and outcome measures in the assessment of AI applications. It is important to ensure uniform and valid decisions regarding the adoption of AI technology with a structured process and tool. MAS-AI can help support these decisions and provide greater transparency for all parties involved.

The use of telemedicine services has increased worldwide during recent years because of national strategies for digitalization of health care and the coronavirus disease 2019 (COVID-19) pandemic. However, healthcare professionals often express uncertainty regarding the effectiveness of telemedicine interventions. The TELEMED database (https://telemedicine.cimt.dk/) was introduced by the Centre for Innovative Medical Technology (CIMT) at Odense University Hospital to ensure that hospital managers, healthcare professionals, and other stakeholders have access to scientific studies of telemedicine interventions.

The database constitutes a structured literature search in PubMed for randomized and non-randomized controlled trials on the use of telemedicine for treating somatic diseases in the hospital setting. The search was conducted by staff members in the health technology assessment unit at CIMT. Identified studies were sorted by first screening titles and abstracts and then by reading full-text versions. The data extracted from the studies included setting, intervention, patient group, type of telemedicine, clinical effect, patient perception, and implementation challenges. The value of each study was also assessed with respect to effectiveness.

A total of 510 articles were selected for data extraction and assessment. The database provides results from 22 different specialties and can be searched using the criteria of medical specialty, country, technology, clinical effect, patient experience, and economic effect. The database serves as an information platform for clinical departments who wish to implement telemedicine services. It has great potential for supporting digital transformation during COVID-19 by providing accessible evidence-based information on patient groups and relevant technologies and their effects. More than 95 percent of the studies in the database that compared telemedicine with a control group showed either statistically significant improvements in clinical outcomes with telemedicine or no statistically significant difference between the two groups.

The TELEMED database provides an easily accessible overview of existing evidence-based telemedicine services. The database is freely available and is expected to be continuously improved and broadened over time.

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.

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.

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.

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.