A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis.

Area of Science:

• Biomedical Data Analysis
• Artificial Intelligence in Healthcare
• Computational Biology

Background:

• Standalone large language models (LLMs) exhibit limitations in reasoning and reliability for complex biomedical and clinical data analysis.
• Emerging multi-agent systems (MASs) offer a novel paradigm to address these limitations through collaborative intelligence.

Purpose of the Study:

• To evaluate the emerging paradigm of multi-agent systems (MASs) for biomedical and clinical data analysis.
• To synthesize findings from recent architectural frameworks like LangGraph, CrewAI, and MCP.
• To examine how specialized agent teams divide labor, utilize precision tools, and cross-verify outputs in MASs.

Main Methods:

• Review and synthesis of recent architectural frameworks for MASs in biomedical data analysis.
• Analysis of agent team specialization, tool utilization, and output cross-verification mechanisms.
• Evaluation of performance gains and operational challenges in implemented MAS applications.

Main Results:

• MAS architectures demonstrate significant performance gains: oncology decision-making accuracy improved from 30.3% to 87.2%.
• MAS achieved 93.2% accuracy on USMLE-style benchmarks and 87.3% accuracy in clinical trial matching, enhancing clinician screening efficiency by 42.6%.
• Critical challenges include a 15-50x higher token consumption ('unreliability tax') and the risk of cascading errors and amplified hallucinations.

Conclusions:

• Multi-agent systems represent a shift toward collaborative intelligence in biomedicine, overcoming standalone LLM limitations.
• Clinical and research adoption necessitates deterministic orchestration and rigorous cost-utility frameworks.
• Ensuring safety and expert-centered oversight is crucial for the successful integration of MAS in healthcare.