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

Introduction
Acute 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 Methods
A 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.

Results
AI 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.

Conclusion
AI 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.