MAS-AI

The use of artificial intelligence (AI) could affect the organisation of treatment, the effect of treatment, the patient's experience of treatment, patient safety, inequality in health, finances, staff consumption and ethics.

At the start of the MAS-AI project, there had been no developed models to assess the effect and consequences of using artificial intelligence, and decision-makers and health professionals were therefore left to themselves in the assessment of a given solution.

Aim

The project aimed to develop a model to help decision-makers to assess and designate AI technologies in image diagnostics. With a thorough model for evaluation, you get an overview of which AI technologies create value, so that you can choose them, and technologies with no or inappropriate effect can be deselected.

The model was developed based on the following steps:

1. A review of existing guides, evaluations and assessments of the value of AI in the field of medical imaging
2. Interviews with leading researchers within AI in Denmark
3. Workshops with decision-makers, patient organisations and researchers

Based on the results from the steps above, the project developed an overall model, which provides administrative and clinical decision-makers with an overview of the consequences of AI technology in the field of medical imaging.

Results

The project developed the model MAS-AI, which is an HTA-based framework to support the introduction of novel AI technologies into healthcare.

The model was developed by a interdisciplinary group of experts, researchers and patient representatives, and it consists of three parts covering nine domains and process factors supporting the assessment.

An early MAS-AI with four areas of assessment

1. The health problem and the current use of technology

2. Technology

3. Ethical aspects

4. Legal aspects

A full MAS-AI with five other assessment areas

5. Safety

6. Clinical aspects

7. Economy

8. Organisation

9. Patient perspectives

as well as five process factors that are recommended to be considered during the evaluation process.

An assessment using MAS-AI can help support decision-making and provide greater transparency for all parties involved.

Participants

The project was carried out in collaboration with the Department of Radiology and the Department of Nuclear Medicine at OUH. In addition, a Canadian validation of MAS-AI is being conducted in collaboration with the Toronto General Hospital Research Institute and Transform HF, including Dr Valeria E. Rac, who is director of the Program for Health System and Technology Evaluation.

The project was also anchored at the Centre for Innovative Medical Technology (CIMT).

Funding

The project was financed by the pool for OUH's Competitive Funds.