Photo Credit: Sopone Nawoot

The following is a summary of “Large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes,” published in the February 2025 issue of the Respiratory Research by Wu et al.

Tuberculous pleural effusion (TPE) is a complex extrapulmonary manifestation of tuberculosis, often requiring invasive and time-consuming diagnostic procedures. Despite advancements in statistical and machine learning models for TPE diagnosis, existing methods are frequently hindered by challenges in data processing, feature selection, and interpretability. This study aims to develop a diagnostic model using ChatGPT-4, a large language model (LLM), and compare its performance with traditional logistic regression and established machine learning algorithms. By leveraging the advanced natural language processing capabilities of LLMs, this approach seeks to improve diagnostic efficiency, enhance feature integration, and provide a more effective, non-invasive solution for early TPE identification. 

A cross-sectional study was conducted, analyzing clinical data from 109 patients diagnosed with TPE and 54 non-TPE patients. From an initial dataset of over 600 variables, 73 key features were selected for model training. The diagnostic performance of ChatGPT-4 was assessed against logistic regression and machine learning models, including k-nearest Neighbors, Random Forest, and Support Vector Machines. Model efficacy was evaluated using key performance metrics such as AUC, F1 score, sensitivity, and specificity. The results demonstrated that the LLM-based model performed comparably to machine learning approaches and significantly outperformed logistic regression in diagnostic accuracy, sensitivity, and specificity. Crucial clinical markers, such as ADA levels and monocyte percentage, were effectively integrated into the LLM framework, enhancing diagnostic precision. 

Additionally, a Python package was developed to facilitate rapid and accessible TPE diagnosis based on clinical parameters. The findings highlight the potential of LLMs as powerful tools for automating and improving complex medical diagnostics. The proposed LLM-based model offers a non-surgical, highly accurate, and cost-effective alternative for early TPE diagnosis. Furthermore, the Python package provides clinicians with a practical and user-friendly application, increasing accessibility and potential adoption in real-world clinical settings. While the results are promising, further validation in larger and more diverse datasets is necessary to refine the model and ensure its robustness across different patient populations. This study underscores the transformative role of LLMs in medical diagnostics, paving the way for their broader implementation in clinical decision-making.

Source: respiratory-research.biomedcentral.com/articles/10.1186/s12931-025-03130-y