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Related Experiment Video

Updated: Mar 15, 2026

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DILIConsult: A Multi-Agent Large Language Model Framework for Evaluating Drug-Induced Liver Injury in ICU Settings.

Alfred Zheng Ting Ho1, Jeren Zheng Feng Law1, Aiwen Wang1,2

  • 1Department of Pharmacy and Pharmaceutical Science, Faculty of Science, National University of Singapore, Singapore City, Singapore.

Pharmacotherapy
|March 14, 2026
PubMed
Summary

This study introduces DILIConsult, an LLM pipeline that effectively analyzes drug-induced liver injury (DILI) cases by overcoming context length limitations. The sequential approach using GPT-4o improved DILI characteristic extraction and case analysis.

Keywords:
chemical and drug‐induced liver injurygenerative artificial intelligencelarge language models

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Drug-Induced Liver Injury (DILI) Research

Background:

  • Large language models (LLMs) offer potential for clinical decision-making but struggle with lengthy patient histories and reference materials for DILI evaluation.
  • Information truncation in conventional LLMs hinders prompt engineering and retrieval-augmented generation (RAG) for complex DILI cases.
  • DILIConsult, an agentic LLM pipeline using GPT-4, is introduced to intelligently parse clinical and drug information for DILI assessment.

Purpose of the Study:

  • To develop and evaluate DILIConsult, an LLM pipeline designed to overcome context length limitations in DILI evaluation.
  • To compare the performance of GPT-4-Turbo and GPT-4o within the DILIConsult framework.
  • To assess the efficacy of full-length versus sequential drug-specific case analysis for DILI evaluation.

Main Methods:

  • Compared GPT-4-Turbo and GPT-4o for extracting DILI characteristics from LiverTox.
  • Tested full-length case analysis versus sequential drug-specific evaluations for DILI cases.
  • Evaluated DILIConsult on DILI cases from the MIMIC-IV dataset using AASLD and EASL criteria, with clinician panel validation.

Main Results:

  • GPT-4o with a sequential approach showed superior performance in DILI characteristic extraction and suspected DILI analysis.
  • DILIConsult achieved a mean rank of 1.66 ± 0.75 for knowledge recall, ranking second for reasoning and medical consensus reflection.
  • The system ranked last for omission of important information (2.07 ± 0.52) and content inaccuracy (2.09 ± 0.72).

Conclusions:

  • DILIConsult demonstrates the potential of LLM-driven workflows to assist clinicians in DILI evaluation.
  • Task division within LLM workflows is crucial for minimizing information loss and improving accuracy.
  • The study highlights the importance of optimizing LLM strategies for complex clinical data analysis.