ArticlePediatric radiology2026
Locally deployed context-aware chatbot outperforms generic large language models for guideline-concordant pediatric imaging recommendations.
Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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Who cites it
3 citing papers in PubMed.
- Are artificial intelligence systems ready for pediatric surgical decision-making? A comparative evaluation of large language models versus pediatric surgeons.Pediatric surgery international · 2026Article
- Prompting the future: artificial intelligence in pediatric radiology.Pediatric radiology · 2026Article
- Perspectives on the future of artificial intelligence in paediatric radiology.Pediatric radiology · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundAccurate modality selection in pediatric imaging is critical, yet adherence to the American College of Radiology (ACR) Appropriateness Criteria remains limited. Large language models (LLMs) offer potential as decision support tools but often lack domain-specific accuracy.
objectiveTo evaluate the performance of a locally run, context-aware chatbot (ped-Llama) based on an open-source LLM for providing personalized pediatric imaging recommendations grounded in ACR guidelines. MATERIALS AND
methodsA simulation-based study was conducted using 50 pediatric clinical scenarios derived from ACR guideline variants. The ped-Llama chatbot, built using a retrieval-augmented generation (RAG) approach with a locally deployed Llama-3.1-8B model, was compared against three generic LLMs (Llama-3.1-8B without RAG, Generative pre-trained transformer-4o [GPT-4o], Claude Opus) and three radiologists (junior resident, senior resident, specialist pediatric radiologist). Each provided imaging recommendations, which were classified as "Usually appropriate," "May be appropriate," or "Usually not appropriate" per ACR guidelines. Modal outputs were analyzed, and consistency across three independent LLM runs was assessed.
resultsped-Llama achieved 80% (40/50) "Usually appropriate" recommendations, outperforming Llama-3.1-8B (46%), GPT-4o (54%), and Claude Opus (46%), and matching the specialist radiologist (76%). When both "Usually appropriate" and "May be appropriate" were accepted, ped-Llama achieved 90% accuracy. Consistency across runs was 72% for ped-Llama versus 44-50% for generic LLMs.
conclusionThis study shows that a locally run, RAG-enabled chatbot based on an open-source LLM can provide guideline-concordant pediatric imaging recommendations with expert-level accuracy. Such systems offer a practical and scalable approach to artificial intelligence-assisted decision support in radiology.
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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.