ArticleJMIRx med2025
Large Language Models for Pediatric Differential Diagnoses in Rural Health Care: Multicenter Retrospective Cohort Study Comparing GPT-3 With Pediatrician Performance.
Article in JMIRx med, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Large-language-models for pediatric diagnosis: Performance evaluation using real-world clinical notes from common and rare cases.Pediatric investigation · 2026Article
- Beyond accuracy metrics: Toward responsible integration of large language models in pediatric diagnostic reasoning.Pediatric investigation · 2026Article
- Large Language Models Using Clinical Text in Pediatrics: A Scoping Review.JAMA network open · 2026Article
- Parents' Perspectives on Artificial Intelligence-Supported Pediatric Healthcare Services: A Descriptive and Correlational Study.Journal of nursing management · 2026Article
- Comparing ChatGPT-3.5, Gemini 2.0, and DeepSeek V3 for pediatric pneumonia learning in medical students.Scientific reports · 2025Article
- Artificial intelligence in pediatric healthcare: current applications, potential, and implementation considerations.Clinical and experimental pediatrics · 2025Article
- Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.Health care analysis : HCA : journal of health philosophy and policy · 2025Article
- Assessing the Accuracy of Diagnostic Capabilities of Large Language Models.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rural health care providers face unique challenges such as limited specialist access and high patient volumes, making accurate diagnostic support tools essential. Large language models like GPT-3 have demonstrated potential in clinical decision support but remain understudied in pediatric differential diagnosis. Objective: This study aims to evaluate the diagnostic accuracy and reliability of a fine-tuned GPT-3 model compared to board-certified pediatricians in rural health care settings. Methods: This multicenter retrospective cohort study analyzed 500 pediatric encounters (ages 0-18 years; n=261, 52.2% female) from rural health care organizations in Central Louisiana between January 2020 and December 2021. The GPT-3 model (DaVinci version) was fine-tuned using the OpenAI application programming interface and trained on 350 encounters, with 150 reserved for testing. Five board-certified pediatricians (mean experience: 12, SD 5.8 years) provided reference standard diagnoses. Model performance was assessed using accuracy, sensitivity, specificity, and subgroup analyses. Results: The GPT-3 model achieved an accuracy of 87.3% (131/150 cases), sensitivity of 85% (95% CI 82%-88%), and specificity of 90% (95% CI 87%-93%), comparable to pediatricians' accuracy of 91.3% (137/150 cases; P=.47). Performance was consistent across age groups (0-5 years: 54/62, 87%; 6-12 years: 47/53, 89%; 13-18 years: 30/35, 86%) and common complaints (fever: 36/39, 92%; abdominal pain: 20/23, 87%). For rare diagnoses (n=20), accuracy was slightly lower (16/20, 80%) but comparable to pediatricians (17/20, 85%; P=.62). Conclusions: This study demonstrates that a fine-tuned GPT-3 model can provide diagnostic support comparable to pediatricians, particularly for common presentations, in rural health care. Further validation in diverse populations is necessary before clinical implementation.
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