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MINI REVIEW article
Front. Aging Neurosci.
Sec. Alzheimer's Disease and Related Dementias
Volume 17 - 2025 | doi: 10.3389/fnagi.2025.1554834
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Introduction: Inadequate primary care infrastructure and training in China and misconceptions about aging lead to high mis-/under-diagnoses and serious time delays for dementia patients, imposing significant burdens on family members and medical carers. Main body: A flowchart integrating rural and urban areas of China dementia care pathway is proposed, especially spotting the obstacles of mis /under-diagnoses and time delays that can be alleviated by data-driven computational strategies.Artificial intelligence (AI) and machine learning models built on dementia data are succinctly reviewed in terms of the roadmap of dementia care from home, community to hospital settings. Challenges and corresponding recommendations to clinical transformation are then reported from the viewpoint of diverse dementia data integrity and accessibility, as well as models' interpretability, reliability, and transparency. Discussion: Dementia cohort study along with developing a center-crossed dementia data platform in China should be strongly encouraged, also data should be publicly accessible where appropriate. Only bey doing so can the challenges be overcome and can AI-enabled dementia research be enhanced, leading to an optimized pathway of dementia care in China. Future policy-guided cooperation between researchers and multi-stakeholders are urgently called for dementia 4E (early-screening, early-assessment, early-diagnosis, and early-intervention).
Keywords: Dementia, Alzheimer's disease, China dementia care pathway, Computational strategy, machine learning, optimization, Interpretability
Received: 03 Jan 2025; Accepted: 17 Feb 2025.
Copyright: © 2025 Lu, Lin, Liu, Chen, Li, Yang, Wang and Ding. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yuhua Wang, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, China
Xuemei Ding, Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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