Start Date: ,
End Date: ,
Click here to access the article.
Extract: Bayesian, neuronal and regression analysis to predict the risk of death remain the most common techniques employed in cardiac and thoracic surgery [1–3]. Artificial intelligence (AI) has emerged as a transformative force across various industries, revolutionizing the way businesses operate, analysing data and making decisions. One area where AI can make a significant impact is in surgical risk modelling. Traditional risk modelling methods have often been limited by their reliance on historical data and static models. In contrast, AI introduces dynamic, adaptive and predictive capabilities that can revolutionize the field of risk management. This editorial explores how AI is transforming risk modelling, examining the benefits, challenges and potential future developments.