Artificial Intelligence in Pediatric Surgery: From Diagnostics and Preoperative Planning to Risk Stratification: A Comprehensive Review of Current Applications
Topic overview
This comprehensive review examines AI applications across four domains in pediatric surgery: diagnostics (achieving 95% accuracy in fracture detection), preoperative planning (3D reconstruction altering strategy in 8% of oncology cases), risk stratification, and error prevention. Despite promising results, most studies lack external validation and prospective evidence, highlighting the need for multicenter datasets and transparent models before widespread clinical adoption.
Key takeaways
- - Deep learning models achieve 95% accuracy in pediatric fracture detection and bone age assessment, but lack external validation across institutions. - AI-driven 3D reconstruction alters surgical strategy in 8% of pediatric oncology cases, though outcome improvements remain unproven. - AI risk models outperform clinical scores for appendicitis and congenital heart surgery, yet fewer than 10% are externally validated. - Most pediatric surgical AI studies are retrospective and single-center, limiting generalizability and clinical translation. - Safe AI adoption requires multicenter pediatric datasets, prospective validation, and interpretable models before routine clinical use.
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How to cite: GlobalCastMD. Artificial Intelligence in Pediatric Surgery: From Diagnostics and Preoperative Planning to Risk Stratification: A Comprehensive Review of Current Applications. GlobalCastMD Medical Library. 2025-11-28. https://library.globalcastmd.com/article/11284
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