Latest Developments in Artificial Intelligence and Machine Learning Models in General Pediatric Surgery

Area of Science:

• Pediatric Surgery
• Medical Artificial Intelligence
• Machine Learning in Healthcare

Background:

• Artificial intelligence (AI) and machine learning (ML) are transforming healthcare, yet their use in pediatric surgery is limited.
• Challenges include rare conditions, small datasets, and a lack of surgeon training in AI.

Purpose of the Study:

• To review current AI and ML applications in general pediatric surgery.
• To focus on key conditions: appendicitis, necrotizing enterocolitis, Hirschsprung's disease, congenital diaphragmatic hernia, and biliary atresia.

Main Methods:

• Narrative review of AI/ML applications in pediatric surgery.
• Summarized AI use in image analysis, diagnostics, prediction, monitoring, and histopathology for five key conditions.
• Highlighted novel tools like explainable AI, NLP, and wearables.

Main Results:

• AI/ML demonstrate promising diagnostic and prognostic capabilities in pediatric surgical conditions.
• Current models often need external validation and standardization.
• Explainable AI, NLP, and wearables are emerging tools.

Conclusions:

• AI and ML have significant potential to enhance pediatric surgical care.
• Widespread adoption necessitates multicenter collaboration, large datasets, and specialized AI education for surgeons.