Large language models (LLMs) have shown promise in enhancing mental health questionnaires by identifying overlapping and redundant symptoms, improving the clarity and efficiency of diagnostic tools. An international study led by Professor Dr. Joseph Kambeitz and Professor Dr. Kai Vogeley from the Faculty of Medicine and University Hospital Cologne demonstrates that AI can help refine existing assessment instruments by analyzing symptom patterns across different mental health conditions. The research reveals that LLMs can detect redundancies in questions, streamline symptom coverage, and improve the generalizability of symptoms across diagnostic categories. This not only makes questionnaires more concise and user-friendly but also supports a more nuanced understanding of mental disorders. In some cases, the AI-driven analysis has even suggested new ways of conceptualizing mental health conditions, potentially leading to more accurate and personalized diagnoses. The findings highlight the potential of artificial intelligence to transform clinical assessment tools, making them more effective and aligned with the complex, overlapping nature of mental health symptoms.