Adithya Shastri

Background: Alzheimer’s disease (AD) is a chronic, progressive neurodegenerative disorder characterized by memory loss, cognitive decline, and behavioral changes. It currently affects over 6 million Americans and is a leading cause of disability in older adults¹³. Diagnosis often occurs late, after significant neuronal damage, which limits the effectiveness of therapeutic interventions¹. Although conventional tools such as the Saint Louis University Mental Status (SLUMS) exam, Mini-Cog test, MRI, PET scans, and cerebrospinal fluid analysis are widely used^6,7, they typically detect the disease only at moderate or advanced stages. Recent literature highlights a growing emphasis on computational methods and biomarker-based approaches that aim to identify AD earlier and guide more personalized treatments^4,5. However, a gap remains in integrating these innovations into routine clinical settings. This review explores recent advancements in AD diagnostics and therapeutics, with particular attention to artificial intelligence (AI), imaging analysis, and predictive modeling techniques that could significantly improve patient outcomes.

Objective: In this literature review, we explored recent advancements in AD diagnostics and therapeutics, with a focus on artificial intelligence (AI), imaging analysis, and predictive modeling techniques that could significantly improve patient outcomes.

Search Methods: A literature review was conducted using databases and sources including PubMed, Nature, Frontiers in Endocrinology, the Alzheimer’s Association, and the U.S. Food and Drug Administration. Search terms included “Alzheimer’s disease,” “early detection,” “biomarkers,” “deep learning,” “transformers,” and “personalized medicine.” Peer-reviewed articles, clinical guidelines, and case studies published between 2019 and 2024 were reviewed for relevance.

Results: Recent developments demonstrate the potential of AI and imaging technologies in improving AD detection and prognosis. Vision transformer models have shown high accuracy in differentiating AD from Parkinson’s disease and healthy controls using MRI scans². Additionally, natural language processing applied to neuropsychological exam recordings has predicted conversion from mild cognitive impairment to AD with an accuracy of 78.5 percent⁸. Personalized treatment plans based on biomarker monitoring are emerging as a future direction, allowing clinicians to better tailor care to individual disease progression¹².

Conclusions: The combination of artificial intelligence, advanced imaging, and biomarker analysis is opening new possibilities for earlier diagnosis and individualized care in Alzheimer’s disease. These tools may help shift clinical practice from late-stage response to early intervention. Future research should focus on clinical validation and implementation of these methods in diverse healthcare settings.

Works Cited:

1. Zhang J, Zhang Y, Wang J, et al. Recent advances in Alzheimer’s disease: mechanisms, clinical trials and new drug development strategies. Signal Transduct Target Ther. 2024;9(1):1. doi:10.1038/s41392-024-01911-3
2. Güven M. Detection of Alzheimer’s and Parkinson’s diseases using deep learning-based various transformers models. Eng Proc. 2024;73(1):4. doi:10.3390/engproc2024073004
3. Franzmeier N, Neitzel J, Rubinski A, et al. Functional brain architecture is associated with the rate of tau accumulation in Alzheimer’s disease. Nat Commun. 2022;13(1):1-12. doi:10.1038/s41467-022-29297-8
4. Dubois B, von Arnim CAF, Burnie N, et al. Biomarkers in Alzheimer’s disease: role in early and differential diagnosis and recognition of atypical variants. Alzheimers Res Ther. 2023;15(1):175. doi:10.1186/s13195-023-01314-6
5. Wang H, Wang J, Wang Y, et al. An interpretable deep learning framework identifies proteomic drivers of Alzheimer’s disease. Nat Aging. 2024;4(1):1-12. doi:10.1038/s43587-023-00567-8
6. Saint Louis University. SLUMS Examination Form. Accessed April 22, 2025. https://www.slu.edu/medicine/internal-medicine/geriatric-medicine/aging-successfully/-pdf/slums-form.pdf
7. Mini-Cog. Mini-Cog Standardized Administration Instructions. Accessed April 22, 2025. https://mini-cog.com/wp-content/uploads/2022/03/Standardized-English-Mini-Cog-1-19-16-EN vl-low-1.pdf
8. Sadowsky CH, Galvin JE. Case report of a 63-year-old patient with Alzheimer disease. Alzheimer Dis Assoc Disord. 2019;33(2):178-180. doi:10.1097/WAD.0000000000000313
9. National Institute on Aging. How Is Alzheimer’s Disease Treated? Accessed April 22, 2025. https://www.nia.nih.gov/health/alzheimers-disease-treated
10. S. Food and Drug Administration. FDA Grants Accelerated Approval for Alzheimer’s Disease Treatment. Published January 6, 2023. Accessed April 22, 2025. https://www.fda.gov/news-events/press-announcements/fda-grants-accelerated-approval-alzheimers-disease-treatment
11. Alzheimer’s Association. Lecanemab Approved for Treatment of Early Alzheimer’s Disease. Accessed April 22, 2025. https://www.alz.org/alzheimers-dementia/treatments/lecanemab-leqembi
12. Li Y, Wang Y, Wang J, et al. The mechanism and efficacy of GLP-1 receptor agonists in the treatment of Alzheimer’s disease. Front Endocrinol (Lausanne). 2022;13:1033479. doi:10.3389/fendo.2022.1033479
13. Alzheimer’s Disease International. Dementia Statistics. Accessed April 22, 2025. https://www.alzint.org/about/dementia-facts-figures/dementia-statistics/
14. Zhang J, Zhang Y, Wang J, et al. Recent advances in Alzheimer’s disease: mechanisms, clinical trials and new drug development strategies. Signal Transduct Target Ther. 2023;8(1):1-20. doi:10.1038/s41392-023-01484-7