Saadeddine Habbal , Maamoon Mian , Musa Imam , Jihane Tahiri , Adam Amor , P. Hemachandra Reddy
{"title":"利用人工智能改变痴呆症护理:早期发现和治疗方面的创新","authors":"Saadeddine Habbal , Maamoon Mian , Musa Imam , Jihane Tahiri , Adam Amor , P. Hemachandra Reddy","doi":"10.1016/j.bosn.2025.05.001","DOIUrl":null,"url":null,"abstract":"<div><div>Dementia, particularly Alzheimer's Disease, continues to be a significant global health concern, driven by increasing prevalence as the population ages. Early detection and accurate diagnosis are essential for improving patient outcomes and mitigating the associated healthcare burden. Artificial intelligence (AI) has emerged as a powerful tool in dementia care, providing innovative approaches to the early detection, diagnosis, and management of these neurodegenerative conditions. This review examines the role of AI in revolutionizing dementia care by focusing on its application in neuroimaging, biomarker identification, predictive modeling, and therapeutic interventions. This narrative review synthesizes recent literature on AI methodologies, including machine learning, deep learning, and neural networks, for their effectiveness in detecting and managing dementia. Emphasis is placed on AI’s integration of multimodal data, such as neuroimaging, genomics, and clinical records, to enhance diagnostic accuracy and predict disease progression. The review also evaluates AI-driven tools for non-invasive screening, personalized treatment planning, and patient monitoring. Findings indicate that AI significantly improves the accuracy and timeliness of dementia diagnoses, often detecting early-stage disease with greater precision than conventional methods. AI’s capacity to analyze complex datasets enables earlier interventions, which are critical for slowing the progression of AD. In the realm of treatment, AI-driven approaches are optimizing personalized care, predicting patient responses to therapies, and advancing drug discovery. The integration of AI into clinical practice is enhancing real-time decision-making and improving overall disease management. In conclusion, AI holds immense potential to transform the future of dementia care. While challenges such as ethical considerations, data privacy, and the need for widespread clinical validation remain, the benefits of AI in early detection, personalized treatment, and improved patient outcomes are substantial. Continued research and cross-disciplinary collaboration will be vital in fully realizing AI’s capabilities in addressing the global dementia epidemic.</div></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"3 ","pages":"Pages 122-133"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Harnessing artificial intelligence for transforming dementia care: Innovations in early detection and treatment\",\"authors\":\"Saadeddine Habbal , Maamoon Mian , Musa Imam , Jihane Tahiri , Adam Amor , P. Hemachandra Reddy\",\"doi\":\"10.1016/j.bosn.2025.05.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Dementia, particularly Alzheimer's Disease, continues to be a significant global health concern, driven by increasing prevalence as the population ages. Early detection and accurate diagnosis are essential for improving patient outcomes and mitigating the associated healthcare burden. Artificial intelligence (AI) has emerged as a powerful tool in dementia care, providing innovative approaches to the early detection, diagnosis, and management of these neurodegenerative conditions. This review examines the role of AI in revolutionizing dementia care by focusing on its application in neuroimaging, biomarker identification, predictive modeling, and therapeutic interventions. This narrative review synthesizes recent literature on AI methodologies, including machine learning, deep learning, and neural networks, for their effectiveness in detecting and managing dementia. Emphasis is placed on AI’s integration of multimodal data, such as neuroimaging, genomics, and clinical records, to enhance diagnostic accuracy and predict disease progression. The review also evaluates AI-driven tools for non-invasive screening, personalized treatment planning, and patient monitoring. Findings indicate that AI significantly improves the accuracy and timeliness of dementia diagnoses, often detecting early-stage disease with greater precision than conventional methods. AI’s capacity to analyze complex datasets enables earlier interventions, which are critical for slowing the progression of AD. In the realm of treatment, AI-driven approaches are optimizing personalized care, predicting patient responses to therapies, and advancing drug discovery. The integration of AI into clinical practice is enhancing real-time decision-making and improving overall disease management. In conclusion, AI holds immense potential to transform the future of dementia care. While challenges such as ethical considerations, data privacy, and the need for widespread clinical validation remain, the benefits of AI in early detection, personalized treatment, and improved patient outcomes are substantial. Continued research and cross-disciplinary collaboration will be vital in fully realizing AI’s capabilities in addressing the global dementia epidemic.</div></div>\",\"PeriodicalId\":100198,\"journal\":{\"name\":\"Brain Organoid and Systems Neuroscience Journal\",\"volume\":\"3 \",\"pages\":\"Pages 122-133\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain Organoid and Systems Neuroscience Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949921625000158\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Organoid and Systems Neuroscience Journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949921625000158","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Harnessing artificial intelligence for transforming dementia care: Innovations in early detection and treatment
Dementia, particularly Alzheimer's Disease, continues to be a significant global health concern, driven by increasing prevalence as the population ages. Early detection and accurate diagnosis are essential for improving patient outcomes and mitigating the associated healthcare burden. Artificial intelligence (AI) has emerged as a powerful tool in dementia care, providing innovative approaches to the early detection, diagnosis, and management of these neurodegenerative conditions. This review examines the role of AI in revolutionizing dementia care by focusing on its application in neuroimaging, biomarker identification, predictive modeling, and therapeutic interventions. This narrative review synthesizes recent literature on AI methodologies, including machine learning, deep learning, and neural networks, for their effectiveness in detecting and managing dementia. Emphasis is placed on AI’s integration of multimodal data, such as neuroimaging, genomics, and clinical records, to enhance diagnostic accuracy and predict disease progression. The review also evaluates AI-driven tools for non-invasive screening, personalized treatment planning, and patient monitoring. Findings indicate that AI significantly improves the accuracy and timeliness of dementia diagnoses, often detecting early-stage disease with greater precision than conventional methods. AI’s capacity to analyze complex datasets enables earlier interventions, which are critical for slowing the progression of AD. In the realm of treatment, AI-driven approaches are optimizing personalized care, predicting patient responses to therapies, and advancing drug discovery. The integration of AI into clinical practice is enhancing real-time decision-making and improving overall disease management. In conclusion, AI holds immense potential to transform the future of dementia care. While challenges such as ethical considerations, data privacy, and the need for widespread clinical validation remain, the benefits of AI in early detection, personalized treatment, and improved patient outcomes are substantial. Continued research and cross-disciplinary collaboration will be vital in fully realizing AI’s capabilities in addressing the global dementia epidemic.