ArticleAnnals of biomedical engineering2024
Examining the Potential of ChatGPT on Biomedical Information Retrieval: Fact-Checking Drug-Disease Associations.
Article in Annals of biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 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
- Large Language Models in Injury Prediction Tools: Simplifying User Interactions and Improving Risk Interpretation.Annals of biomedical engineering · 2026Article
- Pharmacometrics in the Age of Large Language Models: A Vision of the Future.Pharmaceutics · 2025Article
- Bioinformatics and biomedical informatics with ChatGPT: Year one review.Quantitative biology (Beijing, China) · 2024Article
- Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis.Journal of healthcare informatics research · 2024Article
- Article
- Applicability of Online Chat-Based Artificial Intelligence Models to Colorectal Cancer Screening.Digestive diseases and sciences · 2024Article
- Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Large language models facilitating modern molecular biology and novel drug development.Frontiers in pharmacology · 2024Review
- Article
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Authors and funding
6 authors.
Funding
Abstract
Large language models (LLMs) such as ChatGPT have recently attracted significant attention due to their impressive performance on many real-world tasks. These models have also demonstrated the potential in facilitating various biomedical tasks. However, little is known of their potential in biomedical information retrieval, especially identifying drug-disease associations. This study aims to explore the potential of ChatGPT, a popular LLM, in discerning drug-disease associations. We collected 2694 true drug-disease associations and 5662 false drug-disease pairs. Our approach involved creating various prompts to instruct ChatGPT in identifying these associations. Under varying prompt designs, ChatGPT's capability to identify drug-disease associations with an accuracy of 74.6-83.5% and 96.2-97.6% for the true and false pairs, respectively. This study shows that ChatGPT has the potential in identifying drug-disease associations and may serve as a helpful tool in searching pharmacy-related information. However, the accuracy of its insights warrants comprehensive examination before its implementation in medical practice.
Indexed as
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37855948What OpenQuestion holds
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.