⚡ Quick Summary
This systematic review highlights the transformative potential of digital health technologies (DHTs) and artificial intelligence (AI) algorithms in managing Chronic Obstructive Pulmonary Disease (COPD). The study identifies key application areas, including screening, exacerbation prediction, and patient monitoring, showcasing the promise of AI in enhancing COPD care.
🔍 Key Details
- 📊 Dataset: 41 studies included from an initial pool of 265
- 🧩 Data types: Clinical data, patient-reported outcomes, environmental/lifestyle data
- ⚙️ AI Technologies: Machine Learning (ML) and Deep Learning (DL) algorithms
- 🏆 Performance metrics: Promising accuracy in disease progression prediction
🔑 Key Takeaways
- 📊 AI algorithms are increasingly applied in COPD management.
- 💡 Machine Learning was utilized in 34 studies, while Deep Learning appeared in 16.
- 👩🔬 Key application areas include screening and diagnosis, exacerbation prediction, and patient monitoring.
- 🏆 Support vector machines and boosting were the most frequently used ML models.
- 🤖 Deep neural networks and convolutional neural networks were the leading DL models.
- 🌍 Global collaboration is essential for enhancing DHTs’ cost-effectiveness and data-sharing capabilities.
- 🔍 Future research should focus on validating AI algorithms through clinical trials.
📚 Background
Chronic Obstructive Pulmonary Disease (COPD) is a major global health issue, significantly impacting healthcare systems worldwide. The integration of digital health technologies and artificial intelligence presents a promising avenue for improving the management of COPD, offering enhanced predictive capabilities and diagnostic accuracy.
🗒️ Study
This systematic review analyzed studies published up to December 2024, focusing on the application of AI algorithms in digital health for COPD management. The review aimed to identify the types of data utilized and the AI methodologies employed, providing a comprehensive overview of the current landscape in this field.
📈 Results
Out of 265 studies screened, 41 met the inclusion criteria. The analysis revealed a diverse range of data types collected through DHTs, including clinical data and patient-reported outcomes. The review highlighted that machine learning algorithms were predominant, with significant applications in screening, exacerbation prediction, and patient monitoring, demonstrating promising accuracy and performance metrics.
🌍 Impact and Implications
The findings of this review underscore the potential of DHTs and AI algorithms to revolutionize COPD management. By enhancing predictive capabilities and improving patient monitoring, these technologies can lead to better patient outcomes and more efficient healthcare delivery. The implications extend beyond COPD, suggesting a broader application of AI in chronic disease management.
🔮 Conclusion
This systematic review illustrates the significant promise of digital health technologies and AI algorithms in managing COPD. The potential for machine learning to enhance digital health solutions is particularly noteworthy. Future research should prioritize global collaboration, cost-effectiveness studies, and clinical validation to ensure the safe integration of these technologies into routine COPD management.
💬 Your comments
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Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review.
Abstract
BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) represents a significant global health challenge, placing considerable burdens on healthcare systems. The rise of digital health technologies (DHTs) and artificial intelligence (AI) algorithms offers new opportunities to improve COPD predictive capabilities, diagnostic accuracy, and patient management. This systematic review explores the types of data in COPD under DHTs, the AI algorithms employed for data analysis, and identifies key application areas reported in the literature.
METHODS: A systematic search was conducted in PubMed and Web of Science for studies published up to December 2024 that applied AI algorithms in digital health for COPD management. Inclusion criteria focused on original research utilizing AI algorithms and digital health technologies for COPD, while review articles were excluded. Two independent reviewers screened the studies, resolving discrepancies through consensus.
RESULTS: From an initial pool of 265 studies, 41 met the inclusion criteria. Analysis of these studies highlighted a diverse range of data types and modalities collected from DHTs in the COPD context, including clinical data, patient-reported outcomes, and environmental/lifestyle data. Machine learning (ML) algorithms were employed in 34 studies, and deep learning (DL) algorithms in 16. Support vector machines and boosting were the most frequently used ML models, while deep neural networks (DNN) and convolutional neural networks (CNN) were the most commonly used DL models. The review identified three key application domains for AI in COPD: screening and diagnosis (10 studies), exacerbation prediction (22 studies), and patient monitoring (9 studies). Disease progression prediction was a prevalent focus across three domains, with promising accuracy and performance metrics reported.
CONCLUSIONS: Digital health technologies and AI algorithms have a wide range of applications and promise for COPD management. ML models, in particularly, show great potential in improving digital health solutions for COPD. Future research should focus on enhancing global collaboration to explore the cost-effectiveness and data-sharing capabilities of DHTs, enhancing the interpretability of AI models, and validating these algorithms through clinical trials to facilitate their safe integration into the routine COPD management.
Author: [‘Chen Z’, ‘Hao J’, ‘Sun H’, ‘Li M’, ‘Zhang Y’, ‘Qian Q’]
Journal: BMC Med Inform Decis Mak
Citation: Chen Z, et al. Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review. Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review. 2025; 25:77. doi: 10.1186/s12911-025-02870-7