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

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 ,&nbsp;Maamoon Mian ,&nbsp;Musa Imam ,&nbsp;Jihane Tahiri ,&nbsp;Adam Amor ,&nbsp;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 ,&nbsp;Maamoon Mian ,&nbsp;Musa Imam ,&nbsp;Jihane Tahiri ,&nbsp;Adam Amor ,&nbsp;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}
引用次数: 0

摘要

随着人口老龄化,痴呆症,特别是阿尔茨海默病的患病率不断上升,痴呆症仍然是一个重大的全球健康问题。早期发现和准确诊断对于改善患者预后和减轻相关的医疗负担至关重要。人工智能(AI)已成为痴呆症护理的有力工具,为这些神经退行性疾病的早期发现、诊断和管理提供了创新方法。本文通过重点研究人工智能在神经成像、生物标志物识别、预测建模和治疗干预方面的应用,探讨了人工智能在彻底改变痴呆症护理中的作用。这篇叙述性综述综合了最近关于人工智能方法的文献,包括机器学习、深度学习和神经网络,因为它们在检测和管理痴呆症方面的有效性。重点放在人工智能整合多模式数据,如神经影像学、基因组学和临床记录,以提高诊断准确性和预测疾病进展。该综述还评估了用于非侵入性筛查、个性化治疗计划和患者监测的人工智能驱动工具。研究结果表明,人工智能显著提高了痴呆症诊断的准确性和及时性,通常比传统方法更准确地检测出早期疾病。人工智能分析复杂数据集的能力使早期干预成为可能,这对减缓阿尔茨海默病的进展至关重要。在治疗领域,人工智能驱动的方法正在优化个性化护理,预测患者对治疗的反应,并推进药物发现。人工智能与临床实践的融合增强了实时决策,改善了整体疾病管理。总之,人工智能在改变痴呆症护理的未来方面具有巨大的潜力。尽管诸如伦理考虑、数据隐私和广泛临床验证的需求等挑战仍然存在,但人工智能在早期检测、个性化治疗和改善患者预后方面的好处是巨大的。持续的研究和跨学科合作对于充分实现人工智能在应对全球痴呆症流行方面的能力至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信