Application of artificial intelligence in early recognition and differential diagnosis of cognitive impairment

Le résumé fourni par la source

The early identification, etiological differentiation, and longitudinal management of cognitive impairment depend on multidimensional, time-ordered, and highly heterogeneous clinical data. A single biomarker or a conventional scoring instrument is often insufficient to delineate the complex trajectory of pathological progression, whereas artificial intelligence (AI), by virtue of its capacity for nonlinear modeling of high-dimensional features, offers distinct advantages across the full disease continuum. This article reviews the application of AI in the field of cognitive impairment. It covers the role of deep learning models based on structural MRI and cerebrospinal fluid (CSF) indices in predicting the risk of progression from mild cognitive impairment (MCI) to dementia during the pre-diagnostic stage, the value of multimodal radiomics in differentiating Alzheimer's disease (AD) from other dementias, and the utility of machine learning (ML) algorithms in clustering the longitudinal trajectories of cognitive scales. It also summarizes the contributory decision-making role of AI-assisted assessment tools in clinical screening and the outcomes of natural language processing based on electronic health records for the early detection of dementia.Although the clinical adoption of medical AI in cognitive impairment remains constrained by the marked heterogeneity of training samples, insufficient cross-center generalizability, and unresolved issues of informed consent and ethical oversight, strategies such as interpretable modeling and multi-center distributed learning are expected to enhance both the clinical credibility and the transferability of models, thereby advancingearly warning and precision management of cognitive impairment.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.