This study aimed to evaluate the potential cost-effectiveness of implementing a prediction tool for estimating mild cognitive impairment (MCI) to dementia conversion risk. A decision-analytic model was developed to compare the costs and effects of current practice for subjects with MCI to a situation in which the risk of dementia is estimated using a prediction tool. Different scenarios in terms of prediction horizons, prediction characteristics (e.g. sensitivity and specificity), and treatment availability were evaluated. The model was applied to the AI-Mind tool, which is currently under development for predicting MCI to dementia risk. In a clinical situation, with no widely applicable and highly effective disease-modifying treatment available, implementing a dementia risk prediction tool leads to lower QALYs and higher costs compared to current practice without such a prediction tool (9.32 vs 9.36 QALYs and €115,837 vs €115,032 for the analyses in this paper). This loss in QALYs was caused by the impact on quality of life associated with predicted dementia conversion risk. Risk prediction followed by efficient treatment strategies based on the predicted risk could lead to a cost-effective alternative in case of specific treatment characteristics. These findings suggest that standalone (i.e. without highly effective treatment options) use of a dementia risk prediction tool may not be cost-effective, but it could result in a cost-effective alternative in combination with a treatment with favourable efficacy and cost profile.
Keywords: AI-Mind; Alzheimer’s disease; Cost-effectiveness; Dementia; Mild cognitive impairment; Prognosis; Risk prediction; Risk assessment.
© 2026. The Author(s).