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Artificial Intelligence in Alzheimer’s Disease: Evidence Maturity and Clinical Translation Across the Care Pathway
Jin Z, Zhu Z, Chen B, Xu H, Inglis S, Hutchens J, Sun J, Chen F et al.. Preprints.org, 2026-07-30
Topic: Brain imaging, connectivity and assessment
Artificial intelligence (AI) is now a credible translational research platform across the Alzheimer’s disease (AD) pathway, but its clinical value is often difficult to judge from performance metrics alone. This scoping-informed methodological review maps AD-AI evidence across prevention and risk stratification, diagnosis and staging, treatment and therapeutic monitoring, and care and safety support, drawing on PubMed/MEDLINE, IEEE Xplore, ACM Digital Library, ScienceDirect/Scopus-indexed venues, Google Scholar, and citation chaining for literature from 2000 to May 2026. We contribute a decision-centered evidence-maturity framework and apply it to a diagnostic-anchored sample of 31 stage-specific appraisal entries from 29 unique reports, with a PROBAST+AI-informed diagnostic concern screen. Diagnostic AI provides the strongest current evidence base, while prevention, treatment, and care and safety applications show how AI can support earlier risk recognition, anti-amyloid workflow planning, longitudinal monitoring, and caregiver-facing safety systems. In the purposive appraisal sample, explicit reporting of calibration, decision-curve or net-benefit analysis, subgroup fairness, prospective workflow testing, and patient- or caregiver-centered endpoints was uncommon and, for several stage-specific items, absent; exact counts are reported in the appraisal table. The review argues that AD-AI is most persuasive when each model is linked to a user, decision, actionable threshold, uncertainty communication strategy, and outcome, supported by leakage-resistant validation, calibration, decision-curve assessment, subgroup testing, workflow evaluation, and lifecycle governance.
Key findings
- This scoping-informed methodological review maps AD-AI evidence across prevention and risk stratification, diagnosis and staging, treatment and therapeutic monitoring, and care and safety support, drawing on PubMed/MEDLINE, IEEE Xplore, ACM Digital Library, ScienceDirect/Scopus-indexed venues, Google Scholar, and citation chaining for literature from 2000 to May 2026.
- We contribute a decision-centered evidence-maturity framework and apply it to a diagnostic-anchored sample of 31 stage-specific appraisal entries from 29 unique reports, with a PROBAST+AI-informed diagnostic concern screen.
- Diagnostic AI provides the strongest current evidence base, while prevention, treatment, and care and safety applications show how AI can support earlier risk recognition, anti-amyloid workflow planning, longitudinal monitoring, and caregiver-facing safety systems.
- In the purposive appraisal sample, explicit reporting of calibration, decision-curve or net-benefit analysis, subgroup fairness, prospective workflow testing, and patient- or caregiver-centered endpoints was uncommon and, for several stage-specific items, absent; exact counts are reported in the appraisal table.
- The review argues that AD-AI is most persuasive when each model is linked to a user, decision, actionable threshold, uncertainty communication strategy, and outcome, supported by leakage-resistant validation, calibration, decision-curve assessment, subgroup testing, workflow evaluation, and lifecycle governance.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 35 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for sample size, effect reported, 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 22 of the 62 points we could judge it on, which is 35 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Preprints.org) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” 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 | 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 | — / 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 | 22 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 22 of the 62 points we could judge it on is 35 out of 100. |
| Total | 35 / 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 →
Read the original
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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. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Jin, Z., Zhu, Z., Chen, B., Xu, H., Inglis, S., Hutchens, J., et al. (2026). Artificial Intelligence in Alzheimer’s Disease: Evidence Maturity and Clinical Translation Across the Care Pathway. Preprints.org. https://doi.org/10.20944/preprints202607.2192.v1
Vancouver
Jin Z, Zhu Z, Chen B, Xu H, Inglis S, Hutchens J, et al. Artificial Intelligence in Alzheimer’s Disease: Evidence Maturity and Clinical Translation Across the Care Pathway. Preprints.org. 2026. doi:10.20944/preprints202607.2192.v1.
BibTeX
@article{jin2026-49dffc8e,
author = {Jin, Z. and Zhu, Z. and Chen, B. and Xu, H. and Inglis, S. and Hutchens, J. and Sun, J. and Chen, F. and others},
title = {{Artificial Intelligence in Alzheimer’s Disease: Evidence Maturity and Clinical Translation Across the Care Pathway}},
journal = {Preprints.org},
year = {2026},
doi = {10.20944/preprints202607.2192.v1},
url = {https://doi.org/10.20944/preprints202607.2192.v1}
}
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