ArticlePediatric surgery international2026
Are artificial intelligence systems ready for pediatric surgical decision-making? A comparative evaluation of large language models versus pediatric surgeons.
Article in Pediatric surgery international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to compare the diagnostic, investigative, and treatment decisions generated by five contemporary large language models (LLMs) across 50 pediatric surgery clinical scenarios against a reference standard established by pediatric surgery experts.
methodsFifty guideline-based clinical scenarios representing a broad range of pediatric surgical subspecialties were developed. Diagnosis, investigation, and treatment decisions were assessed for each scenario. A consensus reference standard was established by two pediatric surgeons. ChatGPT (GPT-5.5), Claude (Sonnet 4.6), Gemini (3.5 Flash), DeepSeek-V3, and Perplexity (1.0) were evaluated independently using standardized prompts. Accuracy, observed agreement (Po), Gwet's AC1 with 95% confidence intervals, and McNemar tests were used to evaluate model performance and agreement with the pediatric surgeon.
resultsOverall, the pediatric surgeon correctly classified 144 of 150 decisions (Po = 0.960). Claude achieved the highest overall performance (145/150; Po = 0.967), followed by ChatGPT and Gemini (144/150 each; Po = 0.960). DeepSeek-V3 and Perplexity achieved Po values of 0.913 and 0.873, respectively. Claude achieved perfect diagnostic performance (50/50; Po = 1.000). ChatGPT and Gemini performed best for investigations (48/50; Po = 0.960), while Claude, ChatGPT, and Gemini each achieved 48/50 correct treatment decisions (Po = 0.960). No significant differences were identified between the pediatric surgeon and any AI model with respect to diagnostic, investigative, or treatment performance (all p > 0.05). In the overall analysis, only Perplexity demonstrated a performance level significantly different from that of the pediatric surgeon (p = 0.004).
conclusionsLLMs showed high accuracy and strong agreement with expert judgment under standardized guideline-based clinical scenarios, particularly Claude, ChatGPT, and Gemini. The clustering of errors within the investigation and treatment domains suggests that, although these models show considerable promise as clinical decision-support tools, they should be used under clinician supervision rather than as independent decision-makers. CLINICAL
trial registrationNot applicable.
Indexed as
Identifiers
42671649What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.