Machine learning models effectively predict appendicitis in emergencies

This proof-of-concept study reveals that machine learning models can predict appendicitis in patients with acute abdominal pain more accurately than traditional methods. With AUROCs of 0.919 and 0.923 when including laboratory test results, the models outperformed the Alvarado scoring system (AUROC of 0.824) and matched or exceeded the performance of emergency department physicians. These findings suggest that integrating machine learning into emergency workflows could significantly enhance early diagnosis and treatment for suspected appendicitis cases.