Can Artificial Intelligence Revolutionise Surgical Decision-Making for Appendectomy? A Narrative Review

IntroductionAcute appendicitis is a common cause of acute abdomen in secondary care. Despite advancements in diagnostics, misdiagnosis and negative appendectomies remain significant. Artificial Intelligence (AI), particularly machine learning (ML) and deep learning, shows promise in improving diagnostic accuracy.Materials and MethodsA literature review using PubMed and Cochrane databases included studies on AI's role in diagnosing and prognosing appendicitis. Studies relying solely on clinical or radiology reports were excluded.ResultsAI models, particularly random forest (RF), logistic regression (LR), and neural networks (NN), demonstrated high diagnostic accuracy, with RF outperforming others. Machine learning methods like SVM and XGBoost (XGB) were effective in predicting appendicitis prognosis, especially in distinguishing complicated cases. AI models outperformed traditional diagnostic scores, such as the Alvarado score.ConclusionAI has significant potential to enhance the diagnosis and prognosis of acute appendicitis, but challenges in data requirements and standardisation must be addressed for widespread clinical use.