Coding-Free and Privacy-Preserving Agentic Framework for Data-Driven Clinical Research
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 allow...
| Authors | Taehun Kim, Hyeryun Park, Hyeonhoon Lee et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 April 2026 |
| Last updated at source | 21 April 2026 |
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