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Clinical prediction models using artificial intelligence approaches in dementia
Nicola Veronese, Francesco Bolzetta, Livia Gallo, Giorgia Durante, Laura Vernuccio, Carlo Saccaro, Caterina Maria Gambino, Carlo Custodero et al.. Aging Clinical and Experimental Research, 2025-07-25
Topic: Risk, prevention and care in population studies
Among 2699 articles initially screened, 21 studies were included, encompassing over 1 million participants. AI models, extremely heterogenous for their nature, demonstrated good predictive accuracy, with a mean area under the curve of 0.845. While internal validation was conducted in all studies, external validation was limited. Models incorporating ML methods like random forests and support vector machines outperformed traditional approaches. The most used parameters were clinical and cognitive data, whilst data about biomarkers were the less used. Risk of bias was generally low, though calibration and generalizability remained challenges. AI-based prediction models show strong potential for early dementia detection and personalized care. However, their integration into clinical practice requires addressing issues of external validation, data representativeness, and model interpretability. Further research should focus on robust validation and ethical implementation to optimize their utility in dementia care.
Key findings
- Among 2699 articles initially screened, 21 studies were included, encompassing over 1 million participants.
- AI models, extremely heterogenous for their nature, demonstrated good predictive accuracy, with a mean area under the curve of 0.845.
- While internal validation was conducted in all studies, external validation was limited.
- Models incorporating ML methods like random forests and support vector machines outperformed traditional approaches.
- The most used parameters were clinical and cognitive data, whilst data about biomarkers were the less used.
Why it matters
This review of 21 studies found that artificial intelligence models predicted dementia with good accuracy on average, but external validation was limited and calibration and generalizability remained challenges. These are prediction tools, not treatments, cures, or prevention.
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Study details
- Study designsystematic review
- Evidence strengthHigh
How this study scored 59/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Aging Clinical and Experimental Research) |
| Journal's citation record | 1 / 4 | Aging Clinical and Experimental Research: journal h-index 125 (OpenAlex, 2026-10-29) |
| Study design | 12 / 12 | A systematic review of all the studies on the question |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | A review of other studies — randomisation is not credited here |
| Times cited | 2 / 7 | Cited 14 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 8.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 1 / 4 | Cited 14 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Mario Barbagallo: h-index 65 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Karolinska Institutet: institution h-index 1,477 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 7 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40715606) |
| Free to read | 2 / 2 | Free to read |
| Total | 59 / 100 | — |
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Read the original
- DOI: 10.1007/s40520-025-03112-6
- PubMed: 40715606
- Original source →
- Free full text: Aging Clinical and Experimental Research →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Veronese, N., Bolzetta, F., Gallo, L., Durante, G., Vernuccio, L., Saccaro, C., et al. (2025). Clinical prediction models using artificial intelligence approaches in dementia. Aging Clinical and Experimental Research. https://doi.org/10.1007/s40520-025-03112-6
Vancouver
Veronese N, Bolzetta F, Gallo L, Durante G, Vernuccio L, Saccaro C, et al. Clinical prediction models using artificial intelligence approaches in dementia. Aging Clinical and Experimental Research. 2025. doi:10.1007/s40520-025-03112-6. PMID: 40715606.
BibTeX
@article{veronese2025-40715606,
author = {Veronese, Nicola and Bolzetta, Francesco and Gallo, Livia and Durante, Giorgia and Vernuccio, Laura and Saccaro, Carlo and Gambino, Caterina Maria and Custodero, Carlo and others},
title = {{Clinical prediction models using artificial intelligence approaches in dementia}},
journal = {Aging Clinical and Experimental Research},
year = {2025},
doi = {10.1007/s40520-025-03112-6},
pmid = {40715606},
url = {https://doi.org/10.1007/s40520-025-03112-6}
}
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