Abstract
Significant obstacles exist for both patient outcomes and healthcare systems in the form of surgical complications. New approaches to anticipate these issues are being investigated by artificial intelligence (AI) researchers using machine learning, quantile regression forest, and Bayesian optimisation. This paper examines the predictive potential of AI for early postoperative problems, specifically in the context of coronary artery bypass graft surgeries. AI models offer insights into patient hazards through the analysis of large datasets, facilitating better surgical planning and early treatments. Implementing AI is fraught with difficulties, including data quality, clinician trust, and legal barriers, despite encouraging outcomes. In addition to outlining future research possibilities, this paper looks at the promise and constraints of AI in lowering surgical complications. Keywords: Artificial intelligence, surgical complications, coronary artery bypass graft, machine learning
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Research Output Journal of Public Health and Medicine
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
