Multi-Agent AI Systems for Biological and Clinical Data Analysis

Abstract

Multi-agent AI systems, where multiple specialized agents collaborate, are emerging as a powerful approach in biomedicine to tackle complex analytical and clinical tasks that exceed the scope of any single model. Background: This review outlines how orchestrating large language model (LLM) based agents can improve performance and reliability in biomedical data analysis. It surveys new frameworks that coordinate agent teams and highlights state-of-the-art applications in domains such as drug discovery, clinical trial matching, and decision support, where early multi-agent prototypes have achieved higher accuracy or more robust results compared to lone LLMs. Methods: We synthesize findings from recent studies and architectures, categorizing applications and examining how agents divide labor, use tools, and cross-verify each other’s outputs. Results: The review finds that multi-agent strategies yield notable advantages – for example, reducing errors via inter-agent checking and providing more explainable reasoning through transparent dialogues. We also catalog available orchestration platforms and benchmarks driving this field. Conclusions: While multi-agent AI shows promise in augmenting biomedical research and healthcare (by integrating diverse knowledge sources and simulating collaborative problem-solving), ensuring its reliable and ethical deployment will require addressing challenges in verification, scalability, continual learning, and safety. The paper concludes that with careful design and rigorous evaluation, AI agent teams could significantly enhance biomedical intelligence without replacing human experts.

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