An Insight into the Role of Artificial Intelligence in the Early Diagnosis of Alzheimer&rsquo;s Disease

K M Sadat; Soraya

doi:10.25163/biomedical.9110244

Integrative Biomedical Research | Online ISSN 2207-872X

REVIEWS (Open Access)

Previous Next Contents Vol 9 (1)

An Insight into the Role of Artificial Intelligence in the Early Diagnosis of Alzheimer’s Disease

K M Sadat Samin¹*, Soraya Sadek²

+ Author Affiliations

Integrative Biomedical Research (Former Journal of Angiotherapy) 9 (1) 1-8 https://doi.org/10.25163/biomedical.9110244

Submitted: 15 April 2025 Revised: 18 June 2025 Published: 20 June 2025

Abstract

Alzheimer's Disease (AD) emerges as one of the more prevalent chronic neurodegenerative disorders relating to aging. It progressively weakens a person’s mental capabilities and functioning. Currently, over 55 million people smart from this disease crosswise the globe. Additionally, recent projections approximation a staggering rise to 152 million by the year 2050. This underscores the critical need for early diagnosis. Detecting early-stage Alzheimer’s Disease is now being accomplished by conventional Artificial Intelligence methods such as Machine Learning and Deep Learning Techniques. They outperform prior methods. From neuroimaging alone, AI Systems were able to effectively diagnose Alzheimer’s patients with 96% accuracy along with predictive model statements utilizing cognitive assessment data and other biomarkers. For instance, AI models flawlessly predicted 94% of neuroimaging data while AI cognitive tests diagnosed early AD in 91% of cases. Moreover, AI blood biomarker analyses reached 93% accuracy, and AI retinal scans identified early AD signs with 92% accuracy. This type of innovation enables preclinical Alzheimer’s detection, presenting opportunities for timely intervention and improved control of the disease. Despite these promising developments, issues like standardization of data, privacy, and model interpretability remain. On the other hand, the AD diagnostic AI applications inevitability hope worsens faster than fulfills. Advanced AD care could result from earlier and more precise identification, enhanced patient results, and tailored approaches to treatment and proactive measures adopting further research and development AI technologies in the battle against Alzheimer's Disease.

Keywords: Alzheimer’s Disease, Artificial Intelligence, Machine Learning, Early Diagnosis, Neuroimaging, Cognitive Assessment.

References

Anwar, S. M., & Khan, A. (2020). Artificial intelligence and machine learning for healthcare diagnostics: A comprehensive review. Journal of Healthcare Engineering, 2020, 1–19. https://doi.org/10.1155/2020/8900853

Barua Muthsuddy, N. S., Alimullah, M., Alam, I., & Barua Joya, A. (2024). Evaluation of Mimosa pudica leaf extract on oxidative stress and fibrosis in the liver of carbon tetrachloride (CCl4) administered rats. Journal of Biosciences and Experimental Pharmacology, 2(1), 65–77. https://doi.org/10.62624/JBEP00.0011

Biswash, M. A. R., Siddique, M. A. B., Shabuj, M. M. H., Aunni, S. A. A., Rahman, M. M., Das, D. C., & Tufael. (2024). Advancing personalized cancer care: Integrating CRISPR/Cas9 with next-generation sequencing technologies. Journal of Precision Biosciences, 6(1), 1–14. https://doi.org/10.25163/biosciences.10004

Brown, F., & Smith, L. (2019). Retinal imaging and AI: A promising tool for early Alzheimer's diagnosis. Neurodegenerative Diseases, 25(4), 210-222. https://doi.org/10.1155/2019/9871236

Carter, T., Mitchell, C., & Yang, J. (2021). Neurofilament light chain as a biomarker in Alzheimer's Disease: Insights from AI models. Alzheimer's Research & Therapy, 13(3), 56-63. https://doi.org/10.1186/s13195-021-00789-2

Choi, K. H., Lee, Y., & Lim, M. (2021). Artificial intelligence for Alzheimer's disease diagnosis: Recent advances and future challenges. Journal of Neural Engineering, 18(4), 041002. https://doi.org/10.1088/1741-2552/abf665

Ferrara, E. (2024). Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A survey of early trends, datasets, and challenges. Sensors, 24(15), 5045. https://doi.org/10.3390/s24155045

Flavio, D. M. (2024, June 12). M-Health solutions and data analysis to detect and predict risky and adverse health conditions in older adults. https://tesidottorato.depositolegale.it/handle/20.500.14242/143987

Gupta, S., & Arora, A. (2020). Deep learning in neuroimaging: Applications in Alzheimer's disease and beyond. Journal of Medical Imaging and Health Informatics, 10(7), 1649–1656. https://doi.org/10.1166/jmihi.2020.3135

Harrison, D., Smith, G., & Thomas, L. (2022). Multi-modal data integration in Alzheimer's Disease diagnostics using AI. Frontiers in Neurology, 12, 498-512. https://doi.org/10.3389/fneur.2022.723498

Islam, M. R., Yesmin, T., Prapty, A. N., Biswash, M. A. R., Rana, M. S., & Rashid, M. H. O. (2024). Natural environmental sources of resveratrol and its therapeutic role in cancer prevention. Australian Herbal Insight, 7(1), 1–11. https://doi.org/10.25163/ahi.719931

Jahan, T., Halder, T., Islam, A., Patwary, M. U., Mia, R., Amin, S., & Rahman, A. (2024). Genetic disorders: Global impacts, healthcare disparities, and challenges in Bangladesh. Journal of Precision Biosciences, 6(1), 1–17. https://doi.org/10.25163/biosciences.6110044

Johnson, L., Zhao, Q., & Lee, S. (2019). Artificial intelligence in neuroimaging analysis: Applications to Alzheimer's Disease. Neuroinformatics, 23(1), 65-74. https://doi.org/10.1007/s12161-019-01546-5

Jones, D. L., & Miller, M. L. (2022). AI-based imaging in the diagnosis of Alzheimer’s disease: A systematic approach. Journal of Medical Imaging, 30(1), 45–56. https://doi.org/10.1016/j.jmi.2021.09.003

Leifer, B. P. (2003). Early diagnosis of Alzheimer’s disease: clinical and economic benefits. Journal of the American Geriatrics Society, 51(5s2). https://doi.org/10.1046/j.1532-5415.5153.x

Li, R., Wang, X., Lawler, K., Garg, S., Bai, Q., & Alty, J. (2022). Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions. Journal of Biomedical Informatics, 127, 104030. https://doi.org/10.1016/j.jbi.2022.104030

Maresova, P., Mohelska, H., & Kuca, K. (2015). Social and family load of Alzheimer’s disease. Applied Economics, 48(21), 1936–1948. https://doi.org/10.1080/00036846.2015.1111986

Martinez, A., & Zlato, V. (2022). Ethical considerations and privacy concerns in AI-based healthcare systems. Journal of Medical Ethics, 48(1), 23–35. https://doi.org/10.1136/medethics-2021-107625 ?Targeting p38 MAPK: Molecular Docking and Therapeutic Insights for Alzheimer’s Disease Management. (2025, January 2). https://scholar.google.com/citations?view_op=view_citation&hl=en&user=7DVei2cAAAAJ&citation_for_view=7DVei2cAAAAJ:LkGwnXOMwfcC

Martinez, S., Garcia, M., & Lopez, R. (2017). AI-based biomarker analysis for early Alzheimer's detection. Journal of Neurochemistry, 68(5), 527-537. https://doi.org/10.1007/jneurochem.2017.09.014

Monfared, A. a. T., Byrnes, M. J., White, L. A., & Zhang, Q. (2022). Alzheimer’s Disease: Epidemiology and clinical progression. Neurology and Therapy, 11(2), 553–569. https://doi.org/10.1007/s40120-022-00338-8

Nath, N. D., Biswash, M. A. R., Tufael, Debnath, A., Bakar, M. A., & Siddique, M. A. B. (2024). CRISPR-based approaches for diagnosing and treating infectious diseases. Journal of Primeasia, 5(1), 1–8. https://doi.org/10.3329/jpu.v5i1.10166

Nguyen, M., Lee, D., & Smith, P. (2021). The role of machine learning in early Alzheimer's cognitive assessment. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 7(1), 35-42. https://doi.org/10.1016/j.dadm.2020.11.003

Rahman, M. H., Biswash, M. A. R., Siddique, M. A. B., Rahman, M. M., Mou, M. A., Debnath, A., & Fatin, M. (2025). Significance of artificial intelligence in clinical and genomic diagnostics. Journal of Precision Biosciences, 7(1), 1–14. https://doi.org/10.25163/biosciences.7110149

Rahman, M. M., Siddique, M. A. B., Mou, M. A., & Prapty, A. N. (2020). Individualized vaccine development for infectious diseases. Journal of Precision Biosciences, 2(1), 1–10. https://doi.org/10.25163/biosciences.212024

Roberts, M., Li, Y., & Zhang, P. (2021). Enhancing Alzheimer's Disease diagnosis through multi-modal data integration. Journal of Alzheimer's Disease, 68(3), 501-512. https://doi.org/10.3233/JAD-210233

Sanderson, A., & Zink, R. (2021). The role of AI in personalized medicine: Potential applications in Alzheimer's disease. Healthcare Technologies Letters, 8(5), 171–179. https://doi.org/10.1049/htl.2021.0101

Siddique, M. A. B., Asa, T. A., Sohag, M. M. H., Chowdhury, M. S. H., Iqbal, A., & Islam, K. (2021). Molecular diagnosis and evolutionary relationship analysis of plant parasitic tea garden nematodes from different tea estates in Sylhet region of Bangladesh. Journal of Bio-Science, 29(1), 101–109. https://doi.org/10.3329/jbs.v29i0.54826

Siddique, M. A. B., Debnath, A., Nath, N. D., Biswash, M. A. R., & Tufael. (2018). Advancing medical science through nanobiotechnology: Prospects, applications, and future directions. Journal of Primeasia, 1(1), 1–7. https://doi.org/10.25163/primeasia.1110163

Smith, J., Wang, R., & Brown, A. (2018). The role of neuroimaging in early Alzheimer's Disease diagnosis. Journal of Alzheimer's Research, 15(4), 112-120. https://doi.org/10.1016/j.jalzres.2018.01.004

Smith, R. T., Brown, L. P., & Lee, K. J. (2023). Traditional and emerging diagnostic tools for Alzheimer’s disease: A comparative review. Neurodegenerative Disease Reports, 15, 23–31. https://doi.org/10.1007/s40801-023-00243-5

Turner, C., Patel, R., & Green, S. (2020). Advances in AI-driven cognitive testing tools for early Alzheimer's diagnosis. Cognitive Neuropsychology, 12(2), 155-168. https://doi.org/10.1007/s10898-020-01268-2

Wang, Q., Liu, X., & Zhang, S. (2021). Deep learning for Alzheimer's disease detection: A review of methods and applications. Frontiers in Neurology, 12, 701. https://doi.org/10.3389/fneur.2021.00701

Williams, H., Johnson, P., & Lee, J. (2020). Evaluating retinal scans for Alzheimer's Disease using machine learning. Journal of Clinical Neuroscience, 28(2), 98-105. https://doi.org/10.1002/jocn.15448

Zhang, L., Li, J., & Zhou, Y. (2021). Early detection of Alzheimer's disease using deep learning models: A review. Journal of Alzheimer's Disease, 79(3), 125–137. https://doi.org/10.3233/JAD-200578

Zhang, Y., & Wang, X. (2020). AI-powered systems in the detection of Alzheimer's disease: A survey of current research. IEEE Access, 8, 133493–133508. https://doi.org/10.1109/ACCESS.2020.3019385

Zhuang, Y., Wu, F., Chen, C., & Pan, Y. (2017). Challenges and opportunities: from big data to knowledge in AI 2.0. Frontiers of Information Technology & Electronic Engineering, 18(1), 3–14. https://doi.org/10.1631/fitee.1601883

Export Citation

View Dimensions

View Plumx

View Altmetric

0
Save

0
Citation

32
View

0
Share