The Role of Artificial Intelligence and Machine Learning Applications in Emergency Surgery: A Systematic Review of Diagnostic Accuracy and Clinical Outcomes

TL;DR

This paper reviews how AI and machine learning improve diagnostic accuracy and outcomes in emergency surgery, showing promising results across various conditions.

Contribution

A systematic review evaluating AI's role in emergency surgery, focusing on diagnostic accuracy and clinical outcomes compared to conventional methods.

Findings

AI models showed accuracy rates between 72-98% in emergency surgery applications.

AI outperformed conventional methods in acute abdominal pain triage and risk assessment.

19 studies were analyzed across five key areas of emergency surgery, showing AI's broad applicability.

Abstract

Artificial intelligence (AI) refers to computer systems' ability to perform tasks requiring human intelligence. In recent years, AI has rapidly evolved in various fields, including the medical field. The integration of AI into emergency surgical care represents a significant advancement in modern medicine. This field has developed rapidly, particularly since the mid-2010s. The advancement in AI-assisted emergency surgery is built upon several technological pillars, such as deep learning, natural language processing for rapid medical record analysis, and integration with existing hospital information systems. We aim to evaluate the effectiveness of machine learning in identifying emergency patients and the effectiveness of AI methods in diagnosing them compared to conventional methods. We also aim to assess AI's capability in predicting complications and the need for surgical intervention.

The systematic review included English-language research papers published between 2015 and 2025 focusing on human studies. Two independent reviewers analyzed articles following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, screening titles from five significant databases (PubMed, Web of Science, Scopus, Cochrane, and Embase). The final selection process ensured all included studies aligned with the primary research question examining machine learning's impact on emergency care accuracy and efficiency.

This systematic review identified 19 eligible studies from an initial pool of 2791 publications. The results showed the significance of the application of AI across five key areas of emergency surgery: appendicitis management (five studies), emergency abdominal surgery risk assessment (five studies), acute abdominal pain and triage (two studies), bowel obstruction (four studies), and acute conditions of the gallbladder and mesenteric vessels (three studies). Machine learning models demonstrated promising accuracy rates compared to conventional methods in all the different aspects.

This systematic review highlights the promising impact of AI and machine learning across emergency surgery domains. The models demonstrated remarkable accuracy (72-98%) across various applications, from appendicitis diagnosis to cholecystitis detection. Most notably, AI tools showed superior performance in acute abdominal pain triage and risk assessment compared to conventional methods, suggesting their potential to enhance clinical decision-making in emergency surgical settings.

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