From Idea to Data: An Agentic Architecture for the Clinical Trial Life Cycle.

Area of Science:

• Artificial Intelligence in Clinical Research
• Health Informatics
• Computational Biology

Background:

• Clinical research workflows are inherently complex and fragmented, spanning the entire trial life cycle.
• Managing these intricate processes poses significant challenges for researchers.
• Existing systems often lack integrated support across all trial stages.

Purpose of the Study:

• To present a novel multi-agent system designed to support clinical researchers.
• To address the fragmentation and complexity of clinical trial workflows.
• To provide task-dependent AI assistance throughout the clinical trial life cycle.

Main Methods:

• Development of a coordinated multi-agent architecture.
• Integration of multiple large language models (LLMs).
• Application of a structured agentic AI approach for workflow management.

Main Results:

• The system provides task-dependent support across various clinical research stages.
• Demonstrated ability to assist researchers from trial preparation to translation.
• Established a foundation for managing increasingly complex clinical trial processes.

Conclusions:

• The proposed multi-agent system offers a structured AI-driven solution for clinical research.
• This approach can effectively manage the complexity of clinical trial workflows.
• Facilitates improved efficiency and support for researchers in clinical studies.