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Background <p>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.</p> Methods <p>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.</p> Results <p>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 <i>p</i>-values indicating significant improvements over baseline models.</p> Conclusions <p>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.</p>

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Integrative diagnosis of psychiatric conditions using ChatGPT and fMRI data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月19日

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  • Runda Li

摘要

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.