Home › Research › Digital health tools
Advances in Alzheimer’s disease diagnosis with machine learning and deep learning techniques: a comprehensive review
Tazin Alam, Jahanara Suchi, Ankita Saha, Fahad Pathan, Aritra Das, Mohsin Kabir, M. F. Mridha. Artificial Intelligence Review, 2026-02-04
Topic: Amyloid, tau and blood-test diagnosis
Abstract Alzheimer’s disease (AD) is a chronic neurological disease and one of the main causes of dementia in around the world. Traditional diagnostic techniques have limits in terms of subjectivity and resource availability, despite the fact that early and accurate identification of AD is essential for efficient supervision. Deep learning (DL) and Machine Learning (ML) have emerged as powerful tools in medical imaging and have shown promising results in AD detection. This review provides a comprehensive analysis of the latest developments in ML and DL for AD diagnosis, covering essential data sets such as Alzheimer’s Disease Neuroimaging Initiative (ADNI), Open Access Series of Imaging Studies (OASIS), and Australian Imaging, Biomarkers and Lifestyle Study of Ageing (AIBL), as well as preprocessing techniques that enhance data quality. Here, we review some of the significant AD studies and investigate how ML and DL might assist researchers in making an early diagnosis more accurate.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Alam, T., Suchi, J., Saha, A., Pathan, F., Das, A., Kabir, M., et al. (2026). Advances in Alzheimer’s disease diagnosis with machine learning and deep learning techniques: a comprehensive review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-025-11476-4
Vancouver
Alam T, Suchi J, Saha A, Pathan F, Das A, Kabir M, et al. Advances in Alzheimer’s disease diagnosis with machine learning and deep learning techniques: a comprehensive review. Artificial Intelligence Review. 2026. doi:10.1007/s10462-025-11476-4.
BibTeX
@article{alam2026-201e064c,
author = {Alam, Tazin and Suchi, Jahanara and Saha, Ankita and Pathan, Fahad and Das, Aritra and Kabir, Mohsin and Mridha, M. F.},
title = {{Advances in Alzheimer’s disease diagnosis with machine learning and deep learning techniques: a comprehensive review}},
journal = {Artificial Intelligence Review},
year = {2026},
doi = {10.1007/s10462-025-11476-4},
url = {https://doi.org/10.1007/s10462-025-11476-4}
}
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 names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Deep learning (DL) and Machine Learning (ML) have emerged as powerful tools in medical imaging and have shown promising results in AD detection.
- Here, we review some of the significant AD studies and investigate how ML and DL might assist researchers in making an early diagnosis more accurate.
Why it matters
This review summarizes machine learning and deep learning studies that use medical imaging and datasets such as ADNI, OASIS and AIBL to help diagnose Alzheimer's disease earlier and more accurately. It is a review of diagnostic methods and does not report a treatment, cure or prevention.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Evidence strengthLow
How this study scored 41/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Artificial Intelligence Review) |
| Journal's citation record | 2 / 4 | Artificial Intelligence Review: journal h-index 202 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| 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 | 5 / 5 | 2026 — within 1 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 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 8 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 5 / 6 | Cited 38.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 8 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author M. F. Mridha: h-index 42 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Mälardalen University: institution h-index 200 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 41 / 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).
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Related studies in Digital health tools
Also in Digital health tools
178 studies in the library are filed under Digital health tools with no topic tag they share with another; these are this study's neighbours in a fixed tour of them. The Digital health tools hub.