Home › Research › Disparities
Beyond black‐box AI: Interpretable hybrid systems for dementia care
Matthew JY Kang, Wenli Yang, Monica R. Roberts, Byeong Ho Kang, Charles Benjamin Malpas. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring, 2026-01-01
Topic: Risk, prevention and care in population studies
Foundation models (FMs) are entering Alzheimer's disease and related dementias (ADRD), yet bedside impact remains limited. We analyze the interpretability and reliability gaps that impede adoption, including opaque inference, hallucinations in generative models, and weak causal grounding. We argue for hybrid artificial intelligence (AI) that pairs statistical learning with computable clinical knowledge and clinician oversight. We propose a three-level framework for hybrid AI integration: (1) knowledge retrieval with linked citations; (2) contextualized decision support that combines predictive models with actionable plans derived from expert rules; and (3) adaptive optimization through continuous feedback. We demonstrate the potential of hybrid AI using clinical examples, including individual-specific interpretation of novel biomarkers, integration of multimodal data including speech and text, as well as patient-centered digital therapeutics. Finally, we outline pragmatic evaluation aligned with reporting standards, prioritizing adoption, safety, equity, workload, and patient outcomes. This roadmap aims to convert benchmark gains into accountable, interpretable tools for ADRD care. Highlights: Map artificial intelligence (AI) applications across clinical fields and reveal key findings in current literature.Identify how AI supports and advances next-generation technologies in dementia care.Present real-world cases where AI assists clinicians in context-specific scenarios.Examine major challenges and future opportunities in clinical AI adoption.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Kang, M. J. Y., Yang, W., Roberts, M. R., Kang, B. H., & Malpas, C. B. (2026). Beyond black‐box AI: Interpretable hybrid systems for dementia care. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring. https://doi.org/10.1002/dad2.70236
Vancouver
Kang MJY, Yang W, Roberts MR, Kang BH, Malpas CB. Beyond black‐box AI: Interpretable hybrid systems for dementia care. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring. 2026. doi:10.1002/dad2.70236. PMID: 41584894.
BibTeX
@article{kang2026-41584894,
author = {Kang, Matthew JY and Yang, Wenli and Roberts, Monica R. and Kang, Byeong Ho and Malpas, Charles Benjamin},
title = {{Beyond black‐box AI: Interpretable hybrid systems for dementia care}},
journal = {Alzheimer s \& Dementia Diagnosis Assessment \& Disease Monitoring},
year = {2026},
doi = {10.1002/dad2.70236},
pmid = {41584894},
url = {https://doi.org/10.1002/dad2.70236}
}
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
- We propose a three-level framework for hybrid AI integration: (1) knowledge retrieval with linked citations; (2) contextualized decision support that combines predictive models with actionable plans derived from expert rules; and (3) adaptive optimization through continuous feedback.
- We demonstrate the potential of hybrid AI using clinical examples, including individual-specific interpretation of novel biomarkers, integration of multimodal data including speech and text, as well as patient-centered digital therapeutics.
- Finally, we outline pragmatic evaluation aligned with reporting standards, prioritizing adoption, safety, equity, workload, and patient outcomes.
- This roadmap aims to convert benchmark gains into accountable, interpretable tools for ADRD care.
- Highlights: Map artificial intelligence (AI) applications across clinical fields and reveal key findings in current literature.Identify how AI supports and advances next-generation technologies in dementia care.Present real-world cases where AI assists clinicians in context-specific scenarios.Examine major challenges and future opportunities in clinical AI adoption.
Why it matters
This perspective article argues for hybrid artificial intelligence that combines statistical learning with clinical knowledge and clinician oversight, proposing a three-level framework for dementia care. It is a roadmap for developing interpretable tools and does not report a treatment, cure, or prevention for Alzheimer's disease.
New to this subject? Start with our plain-English guide: Disparities →
Study details
- Evidence strengthLow
How this study scored 36/100
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 (Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring) |
| Journal's citation record | 1 / 4 | Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring: journal h-index 86 (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 | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 2 / 6 | Cited 1.9× 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 | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Charles Benjamin Malpas: h-index 40 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | The University of Melbourne: institution h-index 1,342 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41584894) |
| Free to read | 2 / 2 | Free to read |
| Total | 36 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1002/dad2.70236
- PubMed: 41584894
- Original source →
- Free full text: Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring →
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 Disparities
Also tagged “prevalence”
1,224 studies in the library carry the topic tag “prevalence”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 1,224 tagged studies.