Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea
Abstract Background Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current diagnostic gold standard for obstructive sleep apnea is polysomnography, which is labor intensive, requires specialists to utilize, expensive, a...
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SAGE Publishing
2022-04-01
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| Series: | Journal of Otolaryngology - Head and Neck Surgery |
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| Online Access: | https://doi.org/10.1186/s40463-022-00566-w |
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| author | Hannah L. Brennan Simon D. Kirby |
| author_facet | Hannah L. Brennan Simon D. Kirby |
| author_sort | Hannah L. Brennan |
| collection | DOAJ |
| description | Abstract Background Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current diagnostic gold standard for obstructive sleep apnea is polysomnography, which is labor intensive, requires specialists to utilize, expensive, and has accessibility challenges. There are also challenges with awareness and identification of obstructive sleep apnea in the primary care setting. Artificial intelligence systems offer the opportunity for a new diagnostic approach that addresses the limitations of polysomnography and ultimately benefits patients by streamlining the diagnostic expedition. Main body The purpose of this project is to elucidate the barriers that exist in the implementation of artificial intelligence systems into the diagnostic context of obstructive sleep apnea. It is essential to understand these challenges in order to proactively create solutions and establish an efficient adoption of this new technology. The literature regarding the evolution of the diagnosis of obstructive sleep apnea, the role of artificial intelligence in the diagnosis, and the barriers in artificial intelligence implementation was reviewed and analyzed. Conclusion The barriers identified were categorized into different themes including technology, data, regulation, human resources, education, and culture. Many of these challenges are ubiquitous across artificial intelligence implementation in any medical diagnostic setting. Future research directions include developing solutions to the barriers presented in this project. Graphical abstract |
| format | Article |
| id | doaj-art-f2ee8ef60545471b9a44a70ba580071a |
| institution | Kabale University |
| issn | 1916-0216 |
| language | English |
| publishDate | 2022-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Otolaryngology - Head and Neck Surgery |
| spelling | doaj-art-f2ee8ef60545471b9a44a70ba580071a2025-01-02T04:59:12ZengSAGE PublishingJournal of Otolaryngology - Head and Neck Surgery1916-02162022-04-015111910.1186/s40463-022-00566-wBarriers of artificial intelligence implementation in the diagnosis of obstructive sleep apneaHannah L. Brennan0Simon D. Kirby1Faculty of Medicine, Memorial University of Newfoundland and LabradorFaculty of Medicine, Memorial University of Newfoundland and LabradorAbstract Background Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current diagnostic gold standard for obstructive sleep apnea is polysomnography, which is labor intensive, requires specialists to utilize, expensive, and has accessibility challenges. There are also challenges with awareness and identification of obstructive sleep apnea in the primary care setting. Artificial intelligence systems offer the opportunity for a new diagnostic approach that addresses the limitations of polysomnography and ultimately benefits patients by streamlining the diagnostic expedition. Main body The purpose of this project is to elucidate the barriers that exist in the implementation of artificial intelligence systems into the diagnostic context of obstructive sleep apnea. It is essential to understand these challenges in order to proactively create solutions and establish an efficient adoption of this new technology. The literature regarding the evolution of the diagnosis of obstructive sleep apnea, the role of artificial intelligence in the diagnosis, and the barriers in artificial intelligence implementation was reviewed and analyzed. Conclusion The barriers identified were categorized into different themes including technology, data, regulation, human resources, education, and culture. Many of these challenges are ubiquitous across artificial intelligence implementation in any medical diagnostic setting. Future research directions include developing solutions to the barriers presented in this project. Graphical abstracthttps://doi.org/10.1186/s40463-022-00566-wArtificial intelligenceObstructive sleep apneaDiagnosisBarriers |
| spellingShingle | Hannah L. Brennan Simon D. Kirby Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea Journal of Otolaryngology - Head and Neck Surgery Artificial intelligence Obstructive sleep apnea Diagnosis Barriers |
| title | Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea |
| title_full | Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea |
| title_fullStr | Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea |
| title_full_unstemmed | Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea |
| title_short | Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea |
| title_sort | barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea |
| topic | Artificial intelligence Obstructive sleep apnea Diagnosis Barriers |
| url | https://doi.org/10.1186/s40463-022-00566-w |
| work_keys_str_mv | AT hannahlbrennan barriersofartificialintelligenceimplementationinthediagnosisofobstructivesleepapnea AT simondkirby barriersofartificialintelligenceimplementationinthediagnosisofobstructivesleepapnea |