ArticleiScience2025
From knowledge generation to knowledge verification: examining the biomedical generative capabilities of ChatGPT.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- MechAInistic: A Reviewer-Supervised Multi-Agent LLM System for Auditable Mechanistic Drug-Hypothesis Generation.bioRxiv : the preprint server for biology · 2026Article
- Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow.STAR protocols · 2026Article
- Large Language Models in Bio-Ontology Research: A Review.Bioengineering (Basel, Switzerland) · 2025Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The generative capabilities of LLM models offer opportunities for accelerating tasks but raise concerns about the authenticity of the knowledge they produce. We present a computational approach that evaluates the factual accuracy of biomedical knowledge generated by an LLM. Our approach consists of generating disease-centric associations and verifying them using biomedical ontologies. Using ChatGPT, we designed prompt-engineering processes to establish linkages between diseases and related drugs, symptoms, and genes, and assessed consistency across multiple ChatGPT models (e.g., GPT-4, GPT-4o, and GPT-4o-mini). Results demonstrate high accuracy in identifying disease terms (88%-97%), drug names (90%-91%), and genetic information (88%-98%). Symptom term identification was lower (49%-61%) due to informal symptom descriptions. Verification reveals coverage of 89%-91% for disease-drug and disease-gene pairs; symptom-related associations show lower coverage (49%-62%). Despite high term accuracy, generated IDs were often invalid or redundant. GenAI tools can be reliable if used with care. Retrieval Augmented Generation (RAG) may enhance reliability.
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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.