ArticleNature biomedical engineering2026
A collaborative large language model for drug analysis.
Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Navigating uncertainty matters: Evaluating large language models for drug-drug interaction identification.Journal of managed care & specialty pharmacy · 2026Article
- Proposed Context-of-Use Evaluation Framework for Medication Management Tasks Completed by Generative Artificial Intelligence.medRxiv : the preprint server for health sciences · 2026Article
- Beyond the 'Pregnancy Black Box': a global roadmap for artificial intelligence-driven pharmacogenomics in maternal-neonatal health.The pharmacogenomics journal · 2026Review
- Immunological Drug-Drug Interactions in Immune Checkpoint Inhibitor Therapy: Mechanisms, Clinical Evidence, and Artificial Intelligence.Current oncology reports · 2026Review
- Therapeutic rewiring of ceRNA networks: a computational pipeline for drug repurposing in acute kidney injury via circRNA-miRNA-mRNA axis disruption.Human genomics · 2026Review
- Rx-LLM: a benchmarking suite to evaluate safe large language model performance for medication-related tasks.medRxiv : the preprint server for health sciences · 2025Article
- Drug-drug interaction identification using large language models.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs), such as ChatGPT, have substantially helped in understanding human inquiries and generating textual content with human-level fluency. However, directly using LLMs in healthcare applications faces several problems. LLMs are prone to produce hallucinations, or fluent content that appears reasonable and genuine but that is factually incorrect. Ideally, the source of the generated content should be easily traced for clinicians to evaluate. We propose a knowledge-grounded collaborative large language model, DrugGPT, to make accurate, evidence-based and faithful recommendations that can be used for clinical decisions. DrugGPT incorporates diverse clinical-standard knowledge bases and introduces a collaborative mechanism that adaptively analyses inquiries, captures relevant knowledge sources and aligns these inquiries and knowledge sources when dealing with different drugs. We evaluate the proposed DrugGPT on drug recommendation, dosage recommendation, identification of adverse reactions, identification of potential drug-drug interactions and answering general pharmacology questions. DrugGPT outperforms a wide range of existing LLMs and achieves state-of-the-art performance across all metrics with fewer parameters than generic LLMs.
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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.