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Updated: Jun 13, 2026

High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
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A collaborative large language model for drug analysis.

Hongjian Zhou1, Fenglin Liu2, Jinge Wu3

  • 1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.

Nature Biomedical Engineering
|September 23, 2025
PubMed
Summary
This summary is machine-generated.

DrugGPT, a new large language model (LLM), provides accurate, evidence-based clinical recommendations by grounding responses in diverse knowledge bases. It overcomes LLM limitations like hallucinations for safer healthcare applications.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Pharmacology

Background:

  • Large language models (LLMs) demonstrate human-level fluency but pose risks in healthcare due to factual inaccuracies (hallucinations).
  • Ensuring traceability of information sources is crucial for clinical adoption of AI tools.

Purpose of the Study:

  • To develop a knowledge-grounded collaborative LLM, DrugGPT, for accurate and evidence-based clinical decision-making.
  • To address the limitations of generic LLMs in healthcare by enhancing factual accuracy and source traceability.

Main Methods:

  • DrugGPT integrates diverse clinical-standard knowledge bases.
  • A collaborative mechanism adaptively analyzes inquiries, identifies relevant knowledge, and aligns them for drug-related queries.
  • Evaluated on drug/dosage recommendations, adverse reaction/drug-drug interaction identification, and pharmacology questions.

Main Results:

  • DrugGPT demonstrated superior performance compared to existing LLMs across all evaluated metrics.
  • Achieved state-of-the-art results with a reduced parameter count compared to generic LLMs.
  • Ensured accurate, evidence-based, and faithful recommendations.

Conclusions:

  • DrugGPT offers a reliable solution for clinical decision support by mitigating LLM hallucinations.
  • The knowledge-grounded collaborative approach enhances the safety and trustworthiness of AI in pharmaceutical applications.