ArticlePediatric nephrology (Berlin, Germany)2025
Performance evaluation of large language models in pediatric nephrology clinical decision support: a comprehensive assessment.
Article in Pediatric nephrology (Berlin, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 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
- Evaluating Large Reasoning Models Versus Human Multidisciplinary Teams in Lung Cancer Decision-Making: Real-World Study.Journal of medical Internet research · 2026Article
- A brief review of some artificial intelligence methods in nephrology.Pediatric nephrology (Berlin, Germany) · 2026Review
- Ontology-driven generation of parameters for health technology assessment models: a prompt engineering study.International journal of technology assessment in health care · 2026Article
- Assessing large language models in pediatric nephrology: toward more rigorous evaluation.Pediatric nephrology (Berlin, Germany) · 2026Article
- Addressing common questions and misunderstandings about artificial intelligence and large language models in medicine.Pediatric nephrology (Berlin, Germany) · 2026Article
- Evaluating the potential of ChatGPT as an educational decision-support tool for hemodialysis decision-making in nephrology training.Frontiers in medicine · 2026Article
- Evaluating Medical Text Summaries Using Automatic Evaluation Metrics and LLM-as-a-Judge Approach: A Pilot Study.Diagnostics (Basel, Switzerland) · 2025Article
- Promoting Responsible DeepSeek Deployment in Health Care: Scoping Review Comparing Grey and White Literature.Journal of medical Internet research · 2025Article
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Authors and funding
3 authors.
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Abstract
backgroundLarge language models (LLMs) have emerged as potential tools in health care following advancements in artificial intelligence. Despite promising applications across multiple medical specialties, limited research exists regarding LLM implementation in pediatric nephrology. This study evaluates the performance of contemporary LLMs in supporting clinical decision-making processes for practicing pediatric nephrologists.
methodsTen comprehensive clinical cases covering various aspects of pediatric nephrology were designed and validated by experts based on international guidelines. Each case comprised questions addressing diagnosis, biological/imaging explorations, treatments, and logic. Ten LLMs were assessed, including generalist models (Claude, ChatGPT, Gemini, DeepSeek, Mistral, Copilot, Perplexity, Phi 4) and a specialized model (Phi 4 Nomic) fine-tuned using retrieval-augmented generation with validated pediatric nephrology materials. Performance was evaluated based on accuracy, personalization, internal contradictions, hallucinations, and potentially dangerous decisions.
resultsOverall accuracy ranged from 50.8% (Gemini) to 86.9% (Claude), with a mean of 66.24%. Claude significantly outperformed other models (p = 0.01). Personalization scores varied between 50% (ChatGPT) and 85% (Claude). All models exhibited hallucinations (2-8 occurrences) and potentially life-threatening decisions (0-2 occurrences). Domain-specific fine-tuning improved performance across all clinical criteria without enhancing reasoning capabilities. Performance variability was minimal, with higher performing models demonstrating greater consistency.
conclusionsWhile certain LLMs demonstrate promising accuracy in pediatric nephrology applications, persistent challenges including hallucinations and potentially dangerous recommendations preclude autonomous clinical implementation. LLMs may currently serve supportive roles in repetitive tasks, but they should be used under strict supervision in clinical practice. Future advancements addressing hallucination mitigation and interpretability are necessary before broader clinical integration.
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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.