Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer’s disease research
Alzheimer’s disease (AD) is a progressive neurodegenerative condition that impairs memory and cognition, presenting a growing global healthcare burden. Despite major research efforts, no cure exists, and treatments remain focused on symptom relief. This narrative review highlights recent advancement...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1609063/full |
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| author | Zhirong Gu Bin Ge Yuanyuan Wang Yiping Gong Mei Qi |
| author_facet | Zhirong Gu Bin Ge Yuanyuan Wang Yiping Gong Mei Qi |
| author_sort | Zhirong Gu |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is a progressive neurodegenerative condition that impairs memory and cognition, presenting a growing global healthcare burden. Despite major research efforts, no cure exists, and treatments remain focused on symptom relief. This narrative review highlights recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), which enhance early diagnosis, predict disease progression, and support personalized treatment strategies. AI applications are reshaping healthcare by enabling early detection, predicting disease progression, and developing personalized treatment plans. In particular, AI’s ability to analyze complex datasets, including genetic and imaging data, has shown promise in identifying early biomarkers of AD. Additionally, AI-driven cognitive training and rehabilitation programs are emerging as effective tools to improve cognitive function and slow down the progression of cognitive impairment. The paper also discusses the potential of AI in drug discovery and clinical trial optimization, offering new avenues for the development of AD treatments. The paper emphasizes the need for ongoing interdisciplinary collaboration and regulatory oversight to harness AI’s full potential in transforming AD care and improving patient outcomes. |
| format | Article |
| id | doaj-art-23467a7e9b2c4261bfe515b439ddf665 |
| institution | Kabale University |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-23467a7e9b2c4261bfe515b439ddf6652025-08-20T04:01:00ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-08-011710.3389/fnagi.2025.16090631609063Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer’s disease researchZhirong Gu0Bin Ge1Yuanyuan Wang2Yiping Gong3Mei Qi4Department of Pharmacy, Gansu Provincial People’s Hospital, Lanzhou, Gansu, ChinaDepartment of Pharmacy, Gansu Provincial People’s Hospital, Lanzhou, Gansu, ChinaSchool of Pharmacy, Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, ChinaSchool of Pharmacy, Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu, ChinaDepartment of Pharmacy, Gansu Provincial People’s Hospital, Lanzhou, Gansu, ChinaAlzheimer’s disease (AD) is a progressive neurodegenerative condition that impairs memory and cognition, presenting a growing global healthcare burden. Despite major research efforts, no cure exists, and treatments remain focused on symptom relief. This narrative review highlights recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), which enhance early diagnosis, predict disease progression, and support personalized treatment strategies. AI applications are reshaping healthcare by enabling early detection, predicting disease progression, and developing personalized treatment plans. In particular, AI’s ability to analyze complex datasets, including genetic and imaging data, has shown promise in identifying early biomarkers of AD. Additionally, AI-driven cognitive training and rehabilitation programs are emerging as effective tools to improve cognitive function and slow down the progression of cognitive impairment. The paper also discusses the potential of AI in drug discovery and clinical trial optimization, offering new avenues for the development of AD treatments. The paper emphasizes the need for ongoing interdisciplinary collaboration and regulatory oversight to harness AI’s full potential in transforming AD care and improving patient outcomes.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1609063/fullAlzheimer’s diseaseartificial intelligencemachine learningcognitive traininghealthcare innovation |
| spellingShingle | Zhirong Gu Bin Ge Yuanyuan Wang Yiping Gong Mei Qi Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer’s disease research Frontiers in Aging Neuroscience Alzheimer’s disease artificial intelligence machine learning cognitive training healthcare innovation |
| title | Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer’s disease research |
| title_full | Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer’s disease research |
| title_fullStr | Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer’s disease research |
| title_full_unstemmed | Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer’s disease research |
| title_short | Artificial intelligence technologies for enhancing neurofunctionalities: a comprehensive review with applications in Alzheimer’s disease research |
| title_sort | artificial intelligence technologies for enhancing neurofunctionalities a comprehensive review with applications in alzheimer s disease research |
| topic | Alzheimer’s disease artificial intelligence machine learning cognitive training healthcare innovation |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1609063/full |
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