Coding-Free and Privacy-Preserving Agentic Framework for Data-Driven Clinical Research

TL;DR

The paper introduces CARIS, a system that automates clinical research workflows using LLMs, reducing barriers and speeding up data-driven clinical research without requiring coding or extensive data access.

Contribution

It presents a novel framework integrating LLMs with modular tools via MCP for natural language-driven clinical research automation.

Findings

CARIS completed research planning and IRB documentation within four iterations.

Achieved 96% completeness in LLM-based evaluation.

Supported Vibe ML and report generation across three datasets.

Abstract

Clinical data-driven research requires clinical expertise, programming skills, access to patient data, and extensive documentation, creating barriers and slowing the pace for clinicians and external researchers. To address this, we developed the Clinical Agentic Research Intelligence System (CARIS) that automates the workflow: research planning, literature search, cohort construction, Institutional Review Board (IRB) documentation, Vibe Machine Learning (ML), and report generation, with human-in-the-loop refinement. CARIS integrates Large Language Models (LLMs) with modular tools through the Model Context Protocol (MCP), enabling natural language-driven research without coding while allowing users to access only outputs. We evaluated CARIS on three heterogeneous datasets with distinct clinical tasks, where it completed planning and IRB documentation within four iterations, supported Vibe ML, and generated reports, achieving 96% completeness in LLM-based evaluation and 82% in human evaluation. CARIS demonstrates potential to reduce documentation burden and technical barriers, accelerating data-driven clinical research across public and private data environments.