Integrative diagnosis of psychiatric conditions using ChatGPT and fMRI data.

Abstract

Background: Traditional diagnostic methods for psychiatric disorders often rely on subjective assessments, leading to inconsistent diagnoses. Integrating advanced natural language processing (NLP) techniques with neuroimaging data may improve diagnostic accuracy.

Methods: We propose a novel approach that uses ChatGPT to conduct interactive patient interviews, capturing nuanced emotional and psychological data. By analyzing these dialogues using NLP, we generate a comprehensive feature matrix. This matrix, combined with 4D fMRI data, is input into a neural network to predict psychiatric diagnoses. We conducted comparative analysis with survey-based and app-based methods, providing detailed statistical validation.

Results: Our model achieved an accuracy of 85.7%, significantly outperforming traditional methods. Statistical analysis confirmed the superiority of the ChatGPT-based approach in capturing nuanced patient information, with p-values indicating significant improvements over baseline models.

Conclusions: Integrating NLP-driven patient interactions with fMRI data offers a promising approach to psychiatric diagnosis, enhancing precision and reliability. This method could advance clinical practice by providing a more objective and comprehensive diagnostic tool, although more research is needed to generalize these findings.