The Pediatric Surgeon's AI Toolbox: How Large Language Models Like ChatGPT Are Simplifying Practice and Expanding Global Access.

Abstract

Introduction: Pediatric surgeons face substantial administrative workload. Large language models (LLMs) may streamline documentation, family communication, rapid reference, and education, but raise concerns about accuracy, bias, and privacy. This review summarizes practical, near-term uses with clinician oversight.

Materials and methods: Narrative review of LLMs in pediatric surgical workflows and scholarly writing. Sources included MEDLINE/PubMed, Scopus, Embase, Google Scholar, and policy documents (WHO, FDA, EU). Searches spanned January 2015 to August 2025, English only. Peer-reviewed and multicenter studies were prioritized; selected high-signal preprints were labeled. Data screening and extraction were performed by the author; findings were synthesized qualitatively.

Results: Across studies, LLMs reduced drafting time for discharge letters and operative note registries while maintaining clinician-rated quality; they improved readability of consent forms and postoperative instructions and supported patient education. For decision support, general models performed well on structured medical questions, with stronger results when grounded by retrieval. Common limits included coding performance, case-nuance/temporal reasoning, variable translation outside high-resource languages, and citation fabrication without curated sources. Privacy risks stemmed from logging, rare-string memorization, and poorly scoped tool connections. Recommended controls included a clinician-in-the-loop "review and release" workflow, privacy-preserving deployments, version pinning, and ongoing monitoring aligned with early-evaluation guidance.

Conclusion: When outputs are grounded in structured EHR data or curated retrieval and briefly reviewed by clinicians, LLMs can responsibly reduce administrative burden and support communication and education. Early adoption should target high-volume, low-risk, auditable tasks. Future priorities must include multicenter pediatric datasets, transparent benchmarks (accuracy, calibration, equity, time saved), and prospective studies linked to safety outcomes.