The use of AI in Predicting Surgical Complications

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.