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A systematic literature review of automatic Alzheimer's disease detection from speech and language
Petti U, Baker S, Korhonen A.. Journal of the American Medical Informatics Association : JAMIA, 2020-11-01
Topic: Amyloid, tau and blood-test diagnosis
Objective In recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. Materials and methods We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? Results and discussion We identified 33 eligible studies and 5 main findings: participants' demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups. Conclusion The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.
Key findings
- Objective In recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing.
- Results and discussion We identified 33 eligible studies and 5 main findings: participants' demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups.
Why it matters
This systematic review of 33 studies found that automatic speech and language analysis can detect Alzheimer's disease with average classification accuracy of 89%, though participant groups were often unbalanced. It concerns detection methods and does not test any treatment, cure, or prevention for Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 60/100
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 (Journal of the American Medical Informatics Association : JAMIA) |
| Journal's citation record | 2 / 4 | Journal of the American Medical Informatics Association: journal h-index 232 (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 | 2 / 5 | 2020 — within 10 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 169 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 21.1 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 4.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 4 other studies in this library |
| Still being cited | 3 / 4 | Cited 86 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Anna Korhonen: h-index 54 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of Cambridge: institution h-index 1,973 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32929494) |
| Free to read | 2 / 2 | Free to read |
| Total | 60 / 100 | — |
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Read the original
- DOI: 10.1093/jamia/ocaa174
- PubMed: 32929494
- Original source →
- Free full text: Journal of the American Medical Informatics Association →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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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. Check the citation against the original before you use it.
APA 7th
Petti, U., Baker, S., & Korhonen, A. (2020). A systematic literature review of automatic Alzheimer's disease detection from speech and language. Journal of the American Medical Informatics Association : JAMIA. https://doi.org/10.1093/jamia/ocaa174
Vancouver
Petti U, Baker S, Korhonen A. A systematic literature review of automatic Alzheimer's disease detection from speech and language. Journal of the American Medical Informatics Association : JAMIA. 2020. doi:10.1093/jamia/ocaa174. PMID: 32929494.
BibTeX
@article{petti2020-32929494,
author = {Petti, U. and Baker, S. and Korhonen, A.},
title = {{A systematic literature review of automatic Alzheimer's disease detection from speech and language}},
journal = {Journal of the American Medical Informatics Association : JAMIA},
year = {2020},
doi = {10.1093/jamia/ocaa174},
pmid = {32929494},
url = {https://doi.org/10.1093/jamia/ocaa174}
}
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