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Artificial Intelligence for Alzheimer’s Disease Detection Across Neuroimaging and Speech: A Reproducible Cross-Modal Secondary Analysis
Tao J, Caggiano CC.. Research Square, 2026-09-22
Topic: Brain imaging, connectivity and assessment
Artificial-intelligence (AI) systems for Alzheimer’s disease (AD) increasingly use neuroimaging or spontaneous speech, but reported performance is difficult to interpret across studies because cohorts, endpoints, languages, preprocessing, and validation protocols differ. We applied a systematic coding framework to a predefined 51-reference evidence corpus and performed a conservative secondary quantitative synthesis. Twenty-six references met prespecified criteria for primary empirical AI studies: 7 neuroimaging and 19 speech/language studies. Fourteen studies contributed 15 model-level benchmark entries with extractable headline accuracy. Neuroimaging entries ranged from 66.25–99.95% (median 97.74%); speech/language entries ranged from 78.70–92.08% (median 83.81%). These values are descriptive and are not pooled diagnostic-effect estimates. Only 5/26 primary studies (19.2%) explicitly evaluated external, cross-domain, or cross-language generalization, 5/26 (19.2%) used explicit multimodal input, and 4/26 (15.4%) emphasized explainability or uncertainty. Multilingual or cross-lingual evaluation appeared in 3/19 speech/language studies (15.8%). The evidence therefore supports a distinction between high within-study accuracy and clinically credible transportability. Independent validation, patient-disjoint evaluation, calibrated uncertainty, representative cohorts, and linguistically diverse testing should be treated as core design requirements rather than optional extensions. The study-level coding table and benchmark extract are provided for reproducibility and updating.
Key findings
- Only 5/26 primary studies (19.2%) explicitly evaluated external, cross-domain, or cross-language generalization, 5/26 (19.2%) used explicit multimodal input, and 4/26 (15.4%) emphasized explainability or uncertainty.
- Multilingual or cross-lingual evaluation appeared in 3/19 speech/language studies (15.8%).
- The evidence therefore supports a distinction between high within-study accuracy and clinically credible transportability.
- Independent validation, patient-disjoint evaluation, calibrated uncertainty, representative cohorts, and linguistically diverse testing should be treated as core design requirements rather than optional extensions.
- The study-level coding table and benchmark extract are provided for reproducibility and updating.
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Study details
- Effect size99.95%; 97.74%; 92.08%
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 24 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 15 of the 62 points we could judge it on, which is 24 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Research Square) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| 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 | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 15 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 15 of the 62 points we could judge it on is 24 out of 100. |
| Total | 24 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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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
Tao, J., & Caggiano, C. C. (2026). Artificial Intelligence for Alzheimer’s Disease Detection Across Neuroimaging and Speech: A Reproducible Cross-Modal Secondary Analysis. Research Square. https://doi.org/10.21203/rs.3.rs-11106125/v1
Vancouver
Tao J, Caggiano CC. Artificial Intelligence for Alzheimer’s Disease Detection Across Neuroimaging and Speech: A Reproducible Cross-Modal Secondary Analysis. Research Square. 2026. doi:10.21203/rs.3.rs-11106125/v1.
BibTeX
@article{tao2026-eaf460ca,
author = {Tao, J. and Caggiano, C. C.},
title = {{Artificial Intelligence for Alzheimer’s Disease Detection Across Neuroimaging and Speech: A Reproducible Cross-Modal Secondary Analysis}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-11106125/v1},
url = {https://doi.org/10.21203/rs.3.rs-11106125/v1}
}
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