1 Introduction

Mild cognitive impairment (MCI) is an intermediate state between normal aging and Alzheimer's disease, characterized by a decline in memory or other cognitive functions, but does not affect daily life and does not meet the diagnostic criteria for dementia. MCI is a research hotspot because of its potential reversibility, and intervention can improve language cognitive ability and delay cognitive decline. Language ability is an important component of cognitive function, and early changes can help identify cognitive impairments. Against the backdrop of global aging, the proportion of elderly people aged 60 and above in China has reached 18.73%, and the incidence of neurodegenerative diseases is on the rise. MCI is divided into two types: forgetting type and non forgetting type. Forgetting type MCI mainly manifests as memory impairment, while non forgetting type MCI affects attention, language, or visual spatial skills. The causes of MCI include Alzheimer's disease, cerebral small vessel disease, etc. 60% -65% of amnestic MCI patients will develop dementia. The clinical significance of MCI stage lies in early detection and intervention, which can help delay or prevent the occurrence of dementia. The language barrier in MCI patients is an important entry point for detection and intervention. The development of artificial intelligence technology provides new possibilities for the prevention, intervention, and treatment of language disorders in MCI population. This article systematically reviews the research progress on language disorders in the MCI population, focusing on the application of artificial intelligence in the assessment and intervention of MCI language disorders, with the aim of providing assistance for the assessment and treatment of MCI language disorders.

Research progress on language disorders in MCI population

Elaborate on the research progress of language disorders in individuals with mild cognitive impairment (MCI). Petersen et al. first proposed the characteristics and diagnostic criteria of MCI in 1999, which were later optimized by an international working group and included in the 2018 Chinese Dementia and Cognitive Impairment Diagnosis and Treatment Guidelines. The diagnostic criteria for MCI include the discovery of cognitive impairment, objective evidence, independent daily living ability, and failure to meet dementia criteria. Although language disorders are not directly included, they occupy an important position among the five cognitive functions of concern to clinical physicians. There are various methods for determining MCI, including neuropsychological assessment and brain imaging research, among which language ability is a key evaluation area.

3. Artificial Intelligence Empowers Language Impairment Assessment and Intervention in MCI Population

Explored the application of artificial intelligence in the assessment and intervention of language disorders in individuals with mild cognitive impairment (MCI). Domestic and foreign scholars have studied how to use artificial intelligence to dynamically track speech data of MCI population, and conduct intelligent evaluation and monitoring. The commonly used MCI assessment tools currently include the Wechsler Memory Scale and the Auditory Word Learning Test, but the efficiency of corpus collection is low. Artificial intelligence assisted corpus collection and evaluation demonstrate social value and application prospects. For example, the computerized MCI evaluation tool CAMCI has a sensitivity of 80% and a specificity of 74%. The MoCA CC developed in our country has also shown high sensitivity and specificity in identifying MCI. Artificial intelligence technologies such as language recognition, virtual reality, adaptive technology, deep learning, etc. have been applied to MCI language barrier assessment, improving the ecological validity and comprehensiveness of the assessment.

4 Summary

Mild cognitive impairment (MCI) deserves further research in the context of an aging population, especially language barriers in MCI populations. China's research in this field is relatively lagging behind, but the patient population is large, and there is a need to develop testing tools and treatment plans suitable for Chinese patients. The development of artificial intelligence technology provides new opportunities for MCI human-computer interaction systems, which should be guided by the assessment and intervention of language barriers in MCI populations, and explore a theoretical system suitable for China's national conditions and Chinese language context.