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Updated: Apr 1, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Empowering AI data scientists using a multi-agent LLM framework with self-evolving capabilities for autonomous,

Dechao Bu¹, Jingbo Sun^1,2, Kun Li^3,4

¹Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.

Nature Biomedical Engineering

|March 30, 2026

Summary

This summary is machine-generated.

BioMedAgent, a novel artificial intelligence framework, enhances biomedical data analysis by enabling large language models (LLMs) to autonomously use bioinformatics tools. This AI agent achieves high success rates on complex tasks, simplifying scientific exploration.

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Area of Science:

Biomedical Data Analysis
Artificial Intelligence in Science
Bioinformatics Tool Integration

Background:

Large language models (LLMs) offer potential for automating complex tasks and scientific data exploration.
Current LLM applications in biomedical data analysis are constrained by challenges in handling specialized tools and multistep reasoning.
There is a need for advanced AI frameworks that can effectively integrate diverse bioinformatics tools for complex biomedical research.

Purpose of the Study:

To introduce BioMedAgent, a self-evolving LLM multi-agent framework designed for biomedical data analysis.
To enable users to initiate complex tasks using natural language, bypassing the need for computational expertise.
To develop a system capable of learning to use and chain diverse bioinformatics tools into executable workflows.

Main Methods:

Developed a self-evolving LLM multi-agent framework named BioMedAgent.
Implemented interactive exploration and memory retrieval algorithms for tool utilization and workflow chaining.
Trained and evaluated BioMedAgent on the BioMed-AQA benchmark (327 tasks) and the external BixBench dataset.

Main Results:

BioMedAgent achieved a 77% success rate on the BioMed-AQA benchmark, outperforming other LLM agents.
Demonstrated robust generalization capabilities on the external BixBench dataset.
Successfully performed autonomous cross-omics analysis, machine-learning modeling, and pathology image segmentation.

Conclusions:

BioMedAgent represents a significant advancement in applying LLMs to biomedical data analysis, overcoming limitations in tool handling and multistep reasoning.
The framework empowers biomedical researchers by enabling natural language task initiation and automating complex computational workflows.
BioMedAgent shows broad potential for advancing biomedical research and can be extended to other scientific domains requiring sophisticated tool integration.