ArticleNature communications2025
An automated framework for assessing how well LLMs cite relevant medical references.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
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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
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical applications of large language models in knee osteoarthritis: a systematic review.Frontiers in medicine · 2025Pooled it
- MES: A Multi-Agent Evidence Synthesis System for Medical Decision-Making.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in hepatocellular carcinoma diagnosis and AI-assisted treatment support: Current landscape and future directions.Liver research (Beijing, China) · 2026Review
- Tutorial: guidance on the use of large language models for medical research.Nature protocols · 2026Review
- A large language model pipeline for automated citation quality scoring across engineering journal quartiles with expert validation.Scientific reports · 2026Article
- Diagnostic accuracy and citation integrity of four large language models on otolaryngology vignettes.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Evaluating Source-Based Large Language Models for Preclinical Dermatology Education: Comparative Study.JMIR formative research · 2026Article
- Enhancing Physician Resilience to Generative AI: Multilevel Framework for Shared Authority, Verification, and Skill Preservation.Journal of medical Internet research · 2026Article
- Beyond Search Engine Optimization: How Large Language Models Are Redefining Surgeon Visibility.Plastic and reconstructive surgery. Global open · 2026Article
- Article
- WaterRAG: A Multiagent Retrieval-Augmented Generation Framework to Support Water Industry Transitions to Net-Zero.Environmental science & technology · 2026Article
- EcoRxAgent: an AI agent for generating economically substitutable prescriptions.NPJ digital medicine · 2026Article
- Reframing AI for Rare Disease Recognition.Research square · 2026Article
- Can Small Open-Source Language Models With Retrieval-Augmented Generation Match GPT-4 Performance in Breast Cancer Clinical Decision Support?JCO clinical cancer informatics · 2026Article
- Article
- GEN-KnowRD: Reframing AI for Rare Disease Recognition.medRxiv : the preprint server for health sciences · 2026Article
- React-to-Me: A Conversational Interface for Interactive Exploration of the Reactome Pathway Knowledgebase.Research square · 2026Article
- Benchmarking Large Language Models for Drug Combination Alerts: Achieving Expert-Level Reliability via Knowledge Grounding and Contextual Reasoning.Journal of medicinal chemistry · 2026Article
- Recalibrating academic expertise in the age of generative AI.Patterns (New York, N.Y.) · 2026Review
- Benchmarking public large language model responses to patient-facing varicose veins questions: informational quality, verifiability indicators, and readability.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As large language models (LLMs) are increasingly used to address health-related queries, it is crucial that they support their conclusions with credible references. While models can cite sources, the extent to which these support claims remains unclear. To address this gap, we introduce SourceCheckup, an automated agent-based pipeline that evaluates the relevance and supportiveness of sources in LLM responses. We evaluate seven popular LLMs on a dataset of 800 questions and 58,000 pairs of statements and sources on data that represent common medical queries. Our findings reveal that between 50% and 90% of LLM responses are not fully supported, and sometimes contradicted, by the sources they cite. Even for GPT-4o with Web Search, approximately 30% of individual statements are unsupported, and nearly half of its responses are not fully supported. Independent assessments by doctors further validate these results. Our research underscores significant limitations in current LLMs to produce trustworthy medical references.
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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.