Artificial intelligence and natural language processing in modern clinical neuropsychology: A narrative review
Abstract
OBJECTIVES: Advances in natural language processing (NLP) promise to augment traditional neuropsychological assessment by transforming speech and text into objective digital biomarkers. This narrative review synthesizes NLP research, evaluates its incremental diagnostic value across neurodegenerative, neurological, neurodevelopmental and psychiatric disorders, and posits recommendations for adoption in clinical neuropsychology.
METHODS: A scoping search of PubMed, Embase, PsycINFO, Scopus and Web of Science retrieved 56 empirical studies applying NLP within neuropsychological contexts. Manuscripts were critically appraised with attention to data source, linguistic features, modelling approach, validation strategy and clinical utility.
RESULTS: Across neuropsychological syndromes, NLP reliably extracts lexical, syntactic and acoustic markers with pooled area-under-the-curve estimates exceeding 0.85, often outperforming legacy tests while requiring only brief speech samples or existing electronic health-record text. Transformer-based language models further enable real-time documentation support, longitudinal surveillance and personalized feedback. Nonetheless, small homogeneous training sets, limited external calibration and opaque decision pathways threaten generalizability and clinician trust, and implementation of NLP must address algorithmic bias, cultural-linguistic representativeness, ethical privacy standards, and explainability.
CONCLUSIONS: To realize NLP's…
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