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Interpretable machine learning for dementia: A systematic review
Martin SA, Townend FJ, Barkhof F, Cole JH.. Alzheimer's & dementia : the journal of the Alzheimer's Association, 2023-02-03
Topic: Risk, prevention and care in population studies
Introduction Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact. A key challenge is building robust and generalizable models that generate decisions that can be reliably explained. Some models are designed to be inherently "interpretable," whereas post hoc "explainability" methods can be used for other models. Methods Here we sought to summarize the state-of-the-art of interpretable machine learning for dementia. Results We identified 92 studies using PubMed, Web of Science, and Scopus. Studies demonstrate promising classification performance but vary in their validation procedures and reporting standards and rely heavily on popular data sets. Discussion Future work should incorporate clinicians to validate explanation methods and make conclusive inferences about dementia-related disease pathology. Critically analyzing model explanations also requires an understanding of the interpretability methods itself. Patient-specific explanations are also required to demonstrate the benefit of interpretable machine learning in clinical practice.
Key findings
- Results We identified 92 studies using PubMed, Web of Science, and Scopus.
Why it matters
This review of 92 studies found that interpretable machine learning for dementia shows promising classification performance but varies in validation and reporting and relies heavily on popular data sets. It identifies needs for clinician involvement and patient-specific explanations, and reports nothing about treating, curing, or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 66/100
Published in a leading specialty 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 | 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 | 1 / 5 | Rated “low” 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 | 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 | 5 / 7 | Cited 145 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 29.0 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 3 / 6 | Cited 4.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 11 other studies in this library |
| Still being cited | 3 / 4 | Cited 128 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author James Howard Cole: h-index 54 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University College London: institution h-index 2,029 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36735865) |
| Free to read | 2 / 2 | Free to read |
| Total | 66 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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. Check the citation against the original before you use it.
APA 7th
Martin, S. A., Townend, F. J., Barkhof, F., & Cole, J. H. (2023). Interpretable machine learning for dementia: A systematic review. Alzheimer's & dementia : the journal of the Alzheimer's Association. https://doi.org/10.1002/alz.12948
Vancouver
Martin SA, Townend FJ, Barkhof F, Cole JH. Interpretable machine learning for dementia: A systematic review. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2023. doi:10.1002/alz.12948. PMID: 36735865.
BibTeX
@article{martin2023-36735865,
author = {Martin, S. A. and Townend, F. J. and Barkhof, F. and Cole, J. H.},
title = {{Interpretable machine learning for dementia: A systematic review}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2023},
doi = {10.1002/alz.12948},
pmid = {36735865},
url = {https://doi.org/10.1002/alz.12948}
}
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Referenced by this study and also in the alzhub library, so you can read them here.
Later studies that cite this one
11 studies in the alzhub library list this one in their references. Showing the 8 highest-scoring.