LungGPT: A unified multimodal system for interpretable diagnosis and clinical decision support of respiratory diseases
TL;DR
LungGPT provides a standardized framework to enhance clinical workflows and improve patient outcomes in respiratory healthcare by bridging precision diagnostics and rapid decision-making.
Abstract
Summary Respiratory diseases cause significant morbidity, yet diagnosis remains labor intensive and dependent on physician expertise. Here, we present LungGPT, a unified multimodal system trained on 147 million tokens of domain-specific electronic health records from 125,917 participants. LungGPT comprises two modules: LungGPT-Dx for respiratory disease diagnosis and early warning of critical illness, and LungGPT-Ex for interpretable diagnostic reasoning and treatment recommendations. In large-scale evaluations, LungGPT-Dx achieves a macro-average area under the curve (AUC) of 0.852 (95% confidence interval [CI]: 0.839–0.865) across 22 respiratory diseases, with disease-specific AUCs exceeding 0.900 for lung cancer and pulmonary tuberculosis. Crucially, the model further improves early warning of critical illness by incorporating chain-of-thought (CoT) reasoning into textual data and integrating computed tomography (CT) imaging features. LungGPT-Ex generates high-quality, interpretable reasoning that outperforms specialized clinical models and matches advanced general-purpose models such as GPT-4o and DeepSeek-R1 in correctness, completeness, and truthfulness. By bridging precision diagnostics and rapid decision-making, LungGPT provides a standardized framework to enhance clinical workflows and improve patient outcomes in respiratory healthcare.