LLM Questionnaire Completion for Automatic Psychiatric Assessment

Psychiatric evaluation nowadays is heavily dependent on the patient's verbal report about disturbed feelings, thoughts, behaviors and their changes over time. Accordingly, evaluation hinges on two main components: unstructured interviews, which allow patients to express themselves freely under the guidance of open questions, and structured questionnaires, aimed at standardizing the assessment. These methods are outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM) series, which attempts to assign universal scores to individual experiences of mental disorders [1]. However, the inherent complexity of mental health conditions, characterized by a known positive manifold of symptoms and compounded by the subjective nature and potential unreliability of self-reported data (especially from one session to another), along with interviewer biases, make accurate diagnosis challenging. The overlapping symptoms and the instability of mental state, especially in pathological conditions, further complicate the need for precision, precluding an objective and quantitative account of a critical element in the psychiatric evaluation process; the subjective self-experience [2, 3, 4]. The evolution of psychiatric practice is increasingly shaped by the integration of Natural Language Processing (NLP) and machine learning within traditional diagnostic approaches.