Neuropsychiatric disorders affect the lives of individuals from cognitive, emotional, and behavioral aspects, impact the quality of their lives, and even lead to death. Outside the medical area, these diseases have also started to be the subject of investigation in the field of Artificial Intelligence: especially Natural Language Processing (NLP) and Computer Vision. The usage of NLP techniques to understand medical symptoms eases the process of identifying and learning more about language-related aspects of neuropsychiatric conditions, leading to better diagnosis and treatment options. This survey shows the evolution of the detection of linguistic markers specific to a series of neuropsychiatric disorders and symptoms. For each disease or symptom, the article presents a medical description, specific linguistic markers, the results obtained using markers, and datasets. Furthermore, this paper offers a critical analysis of the work undertaken to date and suggests potential directions for future research in the field.

This proof-of-concept corpus study examined the technical feasibility of generating and clinically appraising synthetic psychiatric dialogues about hallucinations. A corpus of 200 dialogues was generated with GPT-4o-mini using synthetic personas, structured prompt attributes, and knowledge graph representations. According to the primary clinician’s annotations, hallucinations were rated as present in $n=94$ dialogues, absent in $n=91$, and No/Unclear in $n=15$. Among the hallucination-positive dialogues, auditory phenomena were recorded in $n=57$, tactile phenomena in $n=16$, olfactory phenomena in $n=14$, and visual phenomena in $n=13$; modalities were not mutually exclusive. The corpus had two clinician annotation layers. The primary psychiatrist provided the annotations used in the descriptive corpus analysis, while a second psychiatrist independently rated the same 200 dialogues in predefined domains for the interrater agreement analyses. Four-category realism showed 75.5% exact agreement (Cohen’s $\kappa =0.549$), and three-category hallucination presence showed 75.0% exact agreement ($\kappa =0.552$). Exploratory within-corpus comparisons examined associations of the primary hallucination-presence rating with realism and exact symptom labels. The resulting corpus is presented as a structured resource for method development and future educational or computational evaluation, not as a validated clinical or decision-support instrument.