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Emerging Trends in Early Dementia Diagnosis: An Analysis on Advanced Machine Learning Approaches
Badal Gami, Manav Agrawal, Rahul Katarya. ACM Computing Surveys, 2025-08-28
Topic: Risk, prevention and care in population studies
Dementia is the waning of cognitive abilities, which is typically seen with the natural aging process and includes issues with memory, language, and problem-solving abilities. Artificial Intelligence (AI) techniques are one viable method for the diagnosis of dementia. Despite recent advances in dementia informatics research and AI, accurate early diagnoses are still far from ideal. This study focuses on showcasing a comprehensive analysis of emerging AI approaches applied to early dementia diagnosis, highlighting trends across neuroimaging, speech, EEG, and clinical data. The proposed work’s main contributions include a summary of the potential challenges and vulnerabilities with dementia informatics research, a wide range of diagnostic issues in dementia care, a descriptive comparison of the elementary manuscripts judged on evaluation parameters such as precision, responsiveness, and definiteness and an offering of a descriptive set of data for developing Machine Learning (ML) and Deep Learning (DL) models. The manuscript also provides a valuable overview of new avenues for informatics research on dementia and advanced ML. The main objective is to fill a gap in the literature by offering an in-depth analysis and overview of the application of AI in dementia research, providing a foundational roadmap for accelerating impactful, data-driven dementia care solutions.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Gami, B., Agrawal, M., & Katarya, R. (2025). Emerging Trends in Early Dementia Diagnosis: An Analysis on Advanced Machine Learning Approaches. ACM Computing Surveys. https://doi.org/10.1145/3764578
Vancouver
Gami B, Agrawal M, Katarya R. Emerging Trends in Early Dementia Diagnosis: An Analysis on Advanced Machine Learning Approaches. ACM Computing Surveys. 2025. doi:10.1145/3764578.
BibTeX
@article{gami2025-fdb3e8c3,
author = {Gami, Badal and Agrawal, Manav and Katarya, Rahul},
title = {{Emerging Trends in Early Dementia Diagnosis: An Analysis on Advanced Machine Learning Approaches}},
journal = {ACM Computing Surveys},
year = {2025},
doi = {10.1145/3764578},
url = {https://doi.org/10.1145/3764578}
}
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
- Despite recent advances in dementia informatics research and AI, accurate early diagnoses are still far from ideal.
- This study focuses on showcasing a comprehensive analysis of emerging AI approaches applied to early dementia diagnosis, highlighting trends across neuroimaging, speech, EEG, and clinical data.
- The proposed work’s main contributions include a summary of the potential challenges and vulnerabilities with dementia informatics research, a wide range of diagnostic issues in dementia care, a descriptive comparison of the elementary manuscripts judged on evaluation parameters such as precision, responsiveness, and definiteness and an offering of a descriptive set of data for developing Machine Learning (ML) and Deep Learning (DL) models.
- The manuscript also provides a valuable overview of new avenues for informatics research on dementia and advanced ML.
- The main objective is to fill a gap in the literature by offering an in-depth analysis and overview of the application of AI in dementia research, providing a foundational roadmap for accelerating impactful, data-driven dementia care solutions.
Why it matters
This review of artificial intelligence approaches for early dementia diagnosis concludes that accurate early diagnoses are still far from ideal. It is an analysis of existing research and does not test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 32/100
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 (ACM Computing Surveys) |
| Journal's citation record | 3 / 4 | ACM Computing Surveys: journal h-index 342 (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 | 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 | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 1 / 6 | Cited 0.6× 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 Rahul Katarya: h-index 34 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Delhi Technological University: institution h-index 206 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Total | 32 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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