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Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions
Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J.. Journal of biomedical informatics, 2022-02-17
Topic: Risk, prevention and care in population studies
Seventy percent of cases are due to Alzheimer's disease (AD) pathology and there is a 10-20 year 'pre-clinical' period before significant cognitive decline occurs. With the advancement of technology, Artificial Intelligence (AI) shows promising results in assisting with detection of early-stage dementia. Results After an initial yield of 1,463 records, 1,444 records were screened after removal of duplication. Compared with traditional cognitive tests, AI-based computerized cognitive tests improve the discrimination sensitivity by around 4% and specificity by around 3%. In terms of speech, conversation and language tests, combining both acoustic features and linguistic features achieve the best result with accuracy around 94%.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Li, R., Wang, X., Lawler, K., Garg, S., Bai, Q., & Alty, J. (2022). Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions. Journal of biomedical informatics. https://doi.org/10.1016/j.jbi.2022.104030
Vancouver
Li R, Wang X, Lawler K, Garg S, Bai Q, Alty J. Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions. Journal of biomedical informatics. 2022. doi:10.1016/j.jbi.2022.104030. PMID: 35183766.
BibTeX
@article{li2022-35183766,
author = {Li, R. and Wang, X. and Lawler, K. and Garg, S. and Bai, Q. and Alty, J.},
title = {{Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions}},
journal = {Journal of biomedical informatics},
year = {2022},
doi = {10.1016/j.jbi.2022.104030},
pmid = {35183766},
url = {https://doi.org/10.1016/j.jbi.2022.104030}
}
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.
Key findings
- Seventy percent of cases are due to Alzheimer's disease (AD) pathology and there is a 10-20 year 'pre-clinical' period before significant cognitive decline occurs.
- With the advancement of technology, Artificial Intelligence (AI) shows promising results in assisting with detection of early-stage dementia.
- Results After an initial yield of 1,463 records, 1,444 records were screened after removal of duplication.
- Compared with traditional cognitive tests, AI-based computerized cognitive tests improve the discrimination sensitivity by around 4% and specificity by around 3%.
- In terms of speech, conversation and language tests, combining both acoustic features and linguistic features achieve the best result with accuracy around 94%.
Why it matters
This scoping review found that AI-based computerized cognitive tests improved discrimination sensitivity by around 4% and specificity by around 3% compared with traditional cognitive tests, and that combining acoustic and linguistic features achieved around 94% accuracy. It is about early detection and does not test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 47/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of biomedical informatics) |
| Journal's citation record | 2 / 4 | Journal of Biomedical Informatics: journal h-index 169 (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 | 3 / 5 | 2022 — 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) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 138 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 23.0 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 4 / 6 | Cited 16.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 12 other studies in this library |
| Still being cited | 3 / 4 | Cited 106 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Jane Alty: h-index 36 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of Tasmania: institution h-index 564 (OpenAlex) |
| Multicenter collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35183766) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 47 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.jbi.2022.104030
- PubMed: 35183766
- Original source →
- Free full text: Journal of Biomedical Informatics →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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