Application of artificial intelligence assessment in diagnosing mild cognitive impairment
10.3760/cma.j.cn371468-20250604-00256
- VernacularTitle:人工智能评估在轻度认知障碍诊断中的应用
- Author:
Shuting YANG
1
;
Ajiao FAN
;
Lan ZHANG
Author Information
1. 兰州大学第一临床医学院，兰州 730000
- Publication Type:Journal Article
- Keywords:
Mild cognitive impairment;
Artificial Intelligence;
Machine learning;
Cognitive assessment;
Auxiliary diagnosis
- From:
Chinese Journal of Behavioral Medicine and Brain Science
2025;34(10):949-954
- CountryChina
- Language:Chinese
-
Abstract:
With the intensification of population aging, the incidence of mild cognitive impairment (MCI) has significantly increased, making early screening and intervention crucial for delaying the progression of dementia. However, traditional scales are susceptible to factors such as cultural and educational levels and the experience of the operator, and they have limitations such as insufficient sensitivity and low standardization. There is an urgent need to develop efficient, objective, and precise assessment methods. This article aims to systematically review the latest research progress, application value, and challenges of artificial intelligence (AI) technology in the intelligent assessment of MCI, and explore its potential to promote the early identification and auxiliary diagnosis of MCI. AI-driven intelligent assessment technologies, especially those based on multimodal data fusion and machine learning /deep learning algorithms, have shown significant advantages in MCI screening and diagnosis, breaking through the bottlenecks of traditional methods and enhancing objectivity, efficiency, and dynamic monitoring capabilities, providing a new paradigm for early precise intervention. Although there are challenges such as data standardization, algorithm transparency, privacy, and insufficient model interpretability, as well as clinical translation, through continuous technological optimization, strengthening ethical norms, and multi-center collaboration, intelligent assessment is expected to become a core tool for future cognitive assessment and early prevention and control of MCI, promoting precise and individualized cognitive health management.
