LungGPT: A unified multimodal system for interpretable diagnosis and clinical decision support of respiratory diseases.

Abstract

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.