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Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review
Borchert RJ, Azevedo T, Badhwar A, Bernal J, Betts M, Bruffaerts R, Burkhart MC, Dewachter I et al.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2023-08-10
Topic: Risk, prevention and care in population studies
Introduction Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
Key findings
- Results A total of 255 studies were identified.
Why it matters
This review of 255 studies on AI and brain imaging for dementia found that most (71%) relied on a single research dataset (ADNI) and that few tested their models on an independent group of patients. Discriminative models performed best at telling Alzheimer's disease apart from healthy controls, but accuracy varied with the patients, scan types and methods used. The authors call for clearer reporting and independent validation before these tools reach practice. The review concerns diagnosis and prognosis only, not treatment or prevention.
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Study details
- Participants255
- Evidence strengthModerate
How this study scored 71/100
Published in a leading specialty journal, and it is a systematic review of all the studies on the question. Scored zero for effect reported because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Alzheimer's & dementia : the journal of the Alzheimer's Association) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29) |
| Study design | 12 / 12 | A systematic review of all the studies on the question (from PubMed type “Systematic Review”) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2023 — within 6 year(s) of the newest study here |
| Sample size | 2 / 4 | 255 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | A review of other studies — randomisation is not credited here |
| Times cited | 5 / 7 | Cited 122 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 24.4 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 3 / 6 | Cited 7.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 9 other studies in this library |
| Still being cited | 3 / 4 | Cited 115 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Timothy Rittman: h-index 47 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Harvard University: institution h-index 3,235 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 28 institution(s) in 9 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37563912) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 71 / 100 | — |
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Read the original
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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
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M., Bruffaerts, R., et al. (2023). Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1002/alz.13412
Vancouver
Borchert RJ, Azevedo T, Badhwar A, Bernal J, Betts M, Bruffaerts R, et al. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2023. doi:10.1002/alz.13412. PMID: 37563912.
BibTeX
@article{borchert2023-37563912,
author = {Borchert, R. J. and Azevedo, T. and Badhwar, A. and Bernal, J. and Betts, M. and Bruffaerts, R. and Burkhart, M. C. and Dewachter, I. and others},
title = {{Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2023},
doi = {10.1002/alz.13412},
pmid = {37563912},
url = {https://doi.org/10.1002/alz.13412}
}
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