To assess the role of artificial intelligence (AI) in the diagnosis and treatment of acute appendicitis, a common surgical emergency.
Key Findings:
AI, particularly machine learning models like random forest, shows promise in diagnosing acute appendicitis with high accuracy, as evidenced by recent studies.
The integration of demographic, clinical, and laboratory data enhances the diagnostic capabilities of AI, leading to better patient outcomes.
Current studies are limited in number and often retrospective, affecting the generalizability of findings and highlighting the need for prospective studies.
Interpretation:
AI has the potential to improve diagnostic accuracy for acute appendicitis, but further research, particularly prospective studies and validation in diverse populations, is needed to confirm these findings and address limitations.
Limitations:
Limited number of studies on AI applications in appendicitis diagnosis, necessitating more comprehensive research.
Many studies are retrospective, which may introduce bias and limit the applicability of results.
Variability in data input and methodology across studies may affect the reliability of AI models.
Conclusion:
AI, especially machine learning techniques, could significantly enhance the diagnosis and treatment of acute appendicitis, but more robust, prospective studies are required to confirm these benefits and address the limitations identified.
