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ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning

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arxiv 2404.14777 v2 pith:LCN3AL6D submitted 2024-04-23 cs.CL cs.LG

Ling Yue , Sixue Xing , Jintai Chen , Tianfan Fu This is my paper · ORCID
classification cs.CLcs.LG
keywords clinicaltrialmulti-agentlanguagesystemclinicalagentlargemethod
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) and multi-agent systems have shown impressive capabilities in natural language tasks but face challenges in clinical trial applications, primarily due to limited access to external knowledge. Recognizing the potential of advanced clinical trial tools that aggregate and predict based on the latest medical data, we propose an integrated solution to enhance their accessibility and utility. We introduce Clinical Agent System (ClinicalAgent), a clinical multi-agent system designed for clinical trial tasks, leveraging GPT-4, multi-agent architectures, LEAST-TO-MOST, and ReAct reasoning technology. This integration not only boosts LLM performance in clinical contexts but also introduces novel functionalities. The proposed method achieves competitive predictive performance in clinical trial outcome prediction (0.7908 PR-AUC), obtaining a 0.3326 improvement over the standard prompt Method. Publicly available code can be found at https://anonymous.4open.science/r/ClinicalAgent-6671.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration

    cs.LG 2024-11 conditional novelty 6.0 of 10

    DrugAgent, a Planner-Instructor multi-agent LLM framework with drug-discovery documentation, beats ReAct by 4.92% ROC-AUC on DTI and exceeds general agents on two of three tasks in its Top3 variant.

  2. Political-LLM: Large Language Models in Political Science

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  3. A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent

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  4. LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models

    cs.CL 2024-12 reject novelty 4.0 of 10

    A multi-agent Llama-3.1-70B system generates supportive clinical cases before voting and reports 77.2% accuracy on a 300-question MedQA sample, about 7% relative above zero-shot baselines.

  5. Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System

    cs.CL 2024-12 conditional novelty 4.0 of 10

    A multi-agent system combining report retrieval, impression generation, and consistency review improves automated radiology impression writing over a single LLM, though the built-in reviewer misses some error types.

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