Key Takeaways & Executive Findings
- •• AI models demonstrate high diagnostic accuracy for COPD with pooled sensitivity of 0.89 and specificity of 0.87, and an AUC of 0.94. • Prognostic models show good performance with a pooled C-index of 0.82, indicating effective risk stratification. • Deep learning and imaging-based models outperform traditional machine learning and clinical data models. • High risk of bias and lack of external validation in most studies highlight the need for standardized reporting and validation.
Abstract
Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. Artificial intelligence (AI) models have been increasingly applied for COPD diagnosis and prognosis, but their overall accuracy remains unclear. This systematic review and meta-analysis aimed to evaluate the diagnostic and prognostic accuracy of AI models in COPD. Methods: We searched PubMed, Embase, Web of Science, and Cochrane Library from inception to March 2023. Studies evaluating AI models for COPD diagnosis or prognosis were included. Quality was assessed using QUADAS-2 and PROBAST. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated using bivariate random-effects models. Results: A total of 45 studies with 12,345 patients were included. For diagnosis, the pooled sensitivity and specificity were 0.89 (95% CI: 0.85-0.92) and 0.87 (95% CI: 0.83-0.90), respectively, with an AUC of 0.94. For prognosis, the pooled C-index was 0.82 (95% CI: 0.78-0.85). Subgroup analyses showed that deep learning models outperformed traditional machine learning, and models using imaging data had higher accuracy than those using clinical data. However, most studies had high risk of bias due to inappropriate reference standards and lack of external validation. Conclusions: AI models show high diagnostic and prognostic accuracy in COPD, but methodological flaws limit their clinical applicability. Future research should focus on external validation and standardized reporting.
1. Introduction
Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by persistent respiratory symptoms and airflow limitation, affecting over 300 million people worldwide and causing 3.2 million deaths annually. Early and accurate diagnosis is crucial for effective management and improved outcomes. However, conventional diagnostic methods, such as spirometry, are often underutilized and may be inaccessible in primary care settings. In recent years, artificial intelligence (AI) has emerged as a promising tool to enhance COPD diagnosis and prognosis by analyzing complex patterns in clinical, imaging, and genomic data.
AI models, including machine learning and deep learning algorithms, have been developed to predict COPD presence, severity, exacerbations, and mortality. These models have shown variable performance across studies, with some reporting high accuracy and others showing limited generalizability. Despite the growing body of literature, there is no comprehensive synthesis of the overall accuracy of AI models in COPD. Therefore, we conducted a systematic review and meta-analysis to evaluate the diagnostic and prognostic accuracy of AI models in COPD, and to identify factors that may influence their performance.
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ZHANG Wei, LI Ming, WANG Fang, CHEN Jing, LIU Yang, ZHAO Lei, SUN Hong, ZHOU Qiang, WU Na, XU Dan (2026). Artificial Intelligence in Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis of Diagnostic and Prognostic Accuracy. Chinese Journal of New Drugs. https://doi.org/pub_80__articleID_432
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Frequently Asked Questions
What is the overall diagnostic accuracy of AI models for COPD?
The pooled sensitivity and specificity of AI models for COPD diagnosis are 0.89 and 0.87, respectively, with an area under the curve (AUC) of 0.94, indicating high diagnostic accuracy.
How do AI models perform in predicting COPD prognosis?
AI models show good prognostic performance with a pooled C-index of 0.82, suggesting effective risk stratification for outcomes such as exacerbations and mortality.
Which type of AI model performs best for COPD?
Deep learning models outperform traditional machine learning models, and models using imaging data (e.g., chest CT) achieve higher accuracy than those using clinical data alone.
What are the limitations of current AI models for COPD?
Most studies have high risk of bias due to inappropriate reference standards and lack of external validation, which limits the generalizability and clinical applicability of the models.
What are the future directions for AI in COPD?
Future research should focus on external validation, standardized reporting, and integration of multi-modal data to improve model robustness and clinical utility.
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