AI Article Synopsis
- Obstructive sleep apnea (OSA) is a prevalent health issue that significantly affects individuals if left untreated, with polysomnography being the traditional, but complex and costly, diagnostic method.* -
- This project aims to identify the obstacles in integrating artificial intelligence (AI) systems for diagnosing OSA, which could enhance the efficiency and accessibility of diagnosis compared to existing methods.* -
- The barriers to AI implementation in OSA diagnosis were grouped into categories such as technology, data, regulation, human resources, education, and culture, highlighting the need for focused research to address these challenges.*
Article 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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9036782 | PMC |
| http://dx.doi.org/10.1186/s40463-022-00566-w | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Accurate predictions of SARS-CoV-2 infectivity from comprehensive analysis.
Elife
December 2024
Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
An unprecedented amount of SARS-CoV-2 data has been accumulated compared with previous infectious diseases, enabling insights into its evolutionary process and more thorough analyses. This study investigates SARS-CoV-2 features as it evolved to evaluate its infectivity. We examined viral sequences and identified the polarity of amino acids in the receptor binding motif (RBM) region.
View Article and Find Full Text PDFEditorial: Genetic and cellular heterogeneity in tumors.
Front Cell Dev Biol
December 2024
Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Navigating the unseen peril: safeguarding medical imaging in the age of AI.
Front Artif Intell
December 2024
Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
In response to the increasing significance of artificial intelligence (AI) in healthcare, there has been increased attention - including a Presidential executive order to create an AI Safety Institute - to the potential threats posed by AI. While much attention has been given to the conventional risks AI poses to cybersecurity, and critical infrastructure, here we provide an overview of some unique challenges of AI for the medical community. Above and beyond obvious concerns about vetting algorithms that impact patient care, there are additional subtle yet equally important things to consider: the potential harm AI poses to its own integrity and the broader medical information ecosystem.
View Article and Find Full Text PDFAutomated landmark-based cat facial analysis and its applications.
Front Vet Sci
December 2024
Information Systems Department, University of Haifa, Haifa, Israel.
Facial landmarks, widely studied in human affective computing, are beginning to gain interest in the animal domain. Specifically, landmark-based geometric morphometric methods have been used to objectively assess facial expressions in cats, focusing on pain recognition and the impact of breed-specific morphology on facial signaling. These methods employed a 48-landmark scheme grounded in cat facial anatomy.
View Article and Find Full Text PDFPredictive Scores for Identifying Chronic Opioid Dependence After General Anesthesia Surgery.
J Pain Res
December 2024
Department of Anesthesiology and Perioperative Medicine, People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, Zhengzhou, Henan, People's Republic of China.
Purpose: To address the prevalence and risk factors of postoperative chronic opioid dependence, focusing on the development of a predictive scoring system to identify high-risk populations.
Methods: We analyzed data from the Taiwan Health Insurance Research Database spanning January 2016 to December 2018, encompassing adults undergoing major elective surgeries with general anesthesia. Patient demographics, surgical details, comorbidities, and preoperative medication use were scrutinized.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!