Evidence map›Paper›PMID 41729454›Full record

ArticleGeroScience2026

Evaluating dementia risk prediction in mild cognitive impairment: an early health technology assessment of the AI-Mind tool.

Robin J Vermeulen, Marcel G M Olde Rikkert, Ron Handels, Ira R J Hebold Haraldsen, Hanna Renvall, Paolo M Rossini, Camillo Marra, Fernando Maestú, Maroeska M Rovers, Tim M Govers

Abstract read
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In one paragraph

Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Robin J VermeulenDepartment of Medical Imaging, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA Nijmegen (route 767), PO Box 9101, 6500, Nijmegen, The Netherlands. robin.vermeulen@radboudumc.nl.ORCID http://orcid.org/0000-0003-3054-8459
Marcel G M Olde RikkertRadboudumc Alzheimer Centre, Department of Geriatrics, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA, Nijmegen (Route 678), Nijmegen, the Netherlands.
Ron HandelsFaculty of Health Medicine and Life Sciences, Department of Psychiatry and Neuropsychology, Alzheimer Centre Limburg, Mental Health and Neuroscience Research Institute, Maastricht University, P.O. box 616, 6200MD, Maastricht, the Netherlands.
Ira R J Hebold HaraldsenDepartment of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway.
Hanna RenvallDepartment of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland.
Paolo M RossiniDepartment of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy.
Camillo MarraMemory Clinic, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Fernando MaestúCentre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain.
Maroeska M RoversDepartment of Medical Imaging, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA Nijmegen (route 767), PO Box 9101, 6500, Nijmegen, The Netherlands.
Tim M GoversDepartment of Medical Imaging, Radboud University Medical Centre, Geert Grooteplein 10, 6525 GA Nijmegen (route 767), PO Box 9101, 6500, Nijmegen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AI-MindAlzheimer’s diseaseCost-effectivenessDementiaMild cognitive impairmentPrognosisRisk assessmentRisk prediction

Identifiers

PMID41729454

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.