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 p...
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BMC
2025-02-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02870-7 |
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| author | Zhenli Chen Jie Hao Haixia Sun Min Li Yuan Zhang Qing Qian |
| author_facet | Zhenli Chen Jie Hao Haixia Sun Min Li Yuan Zhang Qing Qian |
| author_sort | Zhenli Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dbf1828ff1cf4c08ac634d3ce9dedbfe |
| institution | DOAJ |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-dbf1828ff1cf4c08ac634d3ce9dedbfe2025-08-20T03:00:58ZengBMCBMC Medical Informatics and Decision Making1472-69472025-02-0125111610.1186/s12911-025-02870-7Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic reviewZhenli Chen0Jie Hao1Haixia Sun2Min Li3Yuan Zhang4Qing Qian5Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical CollegeInstitute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical CollegeInstitute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of Respiratory Medicine, Xiangya Hospital, Central South UniversityDepartment of Respiratory Medicine, Xiangya Hospital, Central South UniversityInstitute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical CollegeAbstract 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.https://doi.org/10.1186/s12911-025-02870-7Chronic obstructive pulmonary diseaseDigital healthArtificial intelligenceMachine learningDeep learning |
| spellingShingle | Zhenli Chen Jie Hao Haixia Sun Min Li Yuan Zhang Qing Qian Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review BMC Medical Informatics and Decision Making Chronic obstructive pulmonary disease Digital health Artificial intelligence Machine learning Deep learning |
| title | Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review |
| title_full | Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review |
| title_fullStr | Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review |
| title_full_unstemmed | Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review |
| title_short | Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review |
| title_sort | applications of digital health technologies and artificial intelligence algorithms in copd systematic review |
| topic | Chronic obstructive pulmonary disease Digital health Artificial intelligence Machine learning Deep learning |
| url | https://doi.org/10.1186/s12911-025-02870-7 |
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