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An AI‐first framework for multimodal data in Alzheimer's disease and related dementias
Varuna H. Jasodanand, Matteo Bellitti, Vijaya B. Kolachalama. Alzheimer s & Dementia, 2025-09-01
Topic: Amyloid, tau and blood-test diagnosis
Advancing the understanding and management of Alzheimer's disease and related dementias requires integrating and analyzing diverse data modalities. Traditional diagnostic tools, like neuroimaging, provide valuable insights but are limited by accessibility and infrastructure demands. Meanwhile, emerging modalities, including wearable sensors and speech analysis, enable less invasive and more continuous data collection but introduce challenges related to standardization and privacy. The coexistence of these heterogeneous data streams complicates multimodal integration across cohorts, populations, and clinical settings. Current analytical approaches typically require modality-specific preprocessing pipelines and harmonization methods that were not designed to accommodate modern AI-based capabilities, such as multimodal fusion. In this perspective, we propose an "AI-first" strategy for multimodal data integration that aligns data structuring, harmonization, and modeling within a unified set of guiding principles to optimize modern AI development, while remaining flexible enough to support classical analytical approaches. HIGHLIGHTS: Understanding and managing ADRD requires integrating biological, cognitive, and behavioral data across multiple modalities. Incorporating multiple modalities requires new standards for harmonization and interoperability. Current data platforms are not necessarily built to support multimodal fusion or generalizable AI models across diverse ADRD populations. Modern AI models are capable of learning from messy, multimodal, and incomplete data but require infrastructure designed for this purpose. We propose rethinking ADRD data systems to prioritize AI compatibility, enabling scalable tools for early diagnosis and longitudinal care.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Jasodanand, V. H., Bellitti, M., & Kolachalama, V. B. (2025). An AI‐first framework for multimodal data in Alzheimer's disease and related dementias. Alzheimer s & Dementia. https://doi.org/10.1002/alz.70719
Vancouver
Jasodanand VH, Bellitti M, Kolachalama VB. An AI‐first framework for multimodal data in Alzheimer's disease and related dementias. Alzheimer s & Dementia. 2025. doi:10.1002/alz.70719. PMID: 40983947.
BibTeX
@article{jasodanand2025-40983947,
author = {Jasodanand, Varuna H. and Bellitti, Matteo and Kolachalama, Vijaya B.},
title = {{An AI‐first framework for multimodal data in Alzheimer's disease and related dementias}},
journal = {Alzheimer s \& Dementia},
year = {2025},
doi = {10.1002/alz.70719},
pmid = {40983947},
url = {https://doi.org/10.1002/alz.70719}
}
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
- HIGHLIGHTS: Understanding and managing ADRD requires integrating biological, cognitive, and behavioral data across multiple modalities.
- Incorporating multiple modalities requires new standards for harmonization and interoperability.
- Current data platforms are not necessarily built to support multimodal fusion or generalizable AI models across diverse ADRD populations.
- Modern AI models are capable of learning from messy, multimodal, and incomplete data but require infrastructure designed for this purpose.
- We propose rethinking ADRD data systems to prioritize AI compatibility, enabling scalable tools for early diagnosis and longitudinal care.
Why it matters
This perspective proposes an 'AI-first' approach to combining different kinds of data, such as imaging, wearable sensors and speech, for Alzheimer's disease and related dementias. It is a proposal about data standards and infrastructure, not a study of any treatment, prevention or cure.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 51/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for 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) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29) |
| 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 | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 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 | 3 / 7 | Cited 16 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 5.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 10.2× 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 | 2 / 4 | Cited 16 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Vijaya B. Kolachalama: h-index 36 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Boston University: institution h-index 1,456 (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 40983947) |
| Free to read | 2 / 2 | Free to read |
| Total | 51 / 100 | — |
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