Using LLMs to Aid Annotation and Collection of Clinically-Enriched Data in Bipolar Disorder and Schizophrenia

AbstractNatural Language Processing (NLP) in mental health has largely focused on social media data or classification problems, often shifting focus from high caseloads or domain-specific needs of real-world practitioners. This study utilizes a dataset of 644 participants, including those with Bipolar Disorder, Schizophrenia, and Healthy Controls, who completed tasks from a standardized mental health instrument. Clinical annotators were used to label this dataset on five clinical variables. Expert annotations across five clinical variables demonstrated that contempo- rary language models, particularly smaller, fine-tuned models, can enhance data collection and annotation with greater accuracy and trust than larger commercial models. We show that these models can effectively capture nuanced clinical variables, offering a powerful tool for advancing mental health research. We also show that for clinically advanced tasks such as domain-specific annotation LLMs provide wrong labels as compared to a fine-tuned smaller model.