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AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling
Kale M, Wankhede N, Pawar R, Ballal S, Kumawat R, Goswami M, Khalid M, Taksande B et al.. Ageing research reviews, 2024-09-16
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD.
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APA 7th
Kale, M., Wankhede, N., Pawar, R., Ballal, S., Kumawat, R., Goswami, M., et al. (2024). AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling. Ageing research reviews. https://doi.org/10.1016/j.arr.2024.102497
Vancouver
Kale M, Wankhede N, Pawar R, Ballal S, Kumawat R, Goswami M, et al. AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling. Ageing research reviews. 2024. doi:10.1016/j.arr.2024.102497. PMID: 39293530.
BibTeX
@article{kale2024-39293530,
author = {Kale, M. and Wankhede, N. and Pawar, R. and Ballal, S. and Kumawat, R. and Goswami, M. and Khalid, M. and Taksande, B. and others},
title = {{AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling}},
journal = {Ageing research reviews},
year = {2024},
doi = {10.1016/j.arr.2024.102497},
pmid = {39293530},
url = {https://doi.org/10.1016/j.arr.2024.102497}
}
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.
Key findings
- Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature.
Why it matters
This review describes how AI could help detect Alzheimer's earlier through imaging and biomarker analysis, and could help match treatments to individual patients. It does not report new trial results, so it does not yet show that AI changes outcomes for patients.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 56/100
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 (Ageing research reviews) |
| Journal's citation record | 2 / 4 | Ageing Research Reviews: journal h-index 225 (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 | 2024 — 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 | 5 / 7 | Cited 183 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 45.8 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 4 / 6 | Cited 18.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 3 / 4 | Cited 183 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Sushruta Koppula: h-index 34 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Sejong University: institution h-index 388 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39293530) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 56 / 100 | — |
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