AI-augmented clinical decision in paediatric appendicitis: can an AI-generated model improve trainees' diagnostic capability?

Unlabelled: Accurate diagnosis of paediatric appendicitis remains a challenge due to its diverse clinical presentations and reliance on subjective assessments. The integration of artificial intelligence (AI) with an expert's ''clinical sense'' has the potential to improve diagnostic accuracy. In this study, we aimed to evaluate the effectiveness of the Artificial Intelligence Pediatric Appendicitis Decision-tree (AiPAD) model in enhancing the diagnostic capabilities of trainees and compare their performance with that of an expert supervisor.

The Development and Validation of Artificial Intelligence Pediatric Appendicitis Decision-Tree for Children 0 to 12 Years Old.

Introduction:  Diagnosing appendicitis in young children (0-12 years) still poses a special difficulty despite the advent of radiological investigations. Few scoring models have evolved and been applied worldwide, but with significant fluctuations in accuracy upon validation.

Aim:  To utilize artificial intelligence (AI) techniques to develop and validate a diagnostic model based on clinical and laboratory parameters only (without imaging), in addition to prospective validation to confirm the findings.