Building Explainable RAG-based Clinical Decision Support

Authors

Irina Pecherskaia, Dakota State UniversityFollow
Jason Mixon, Dakota State University
Andrew Behrens, Dakota State UniversityFollow
Andrew Smith, Dakota State UniversityFollow

Files

Description

The objective of this research is to demonstrate how theoretical Explainable AI (XAI) principles are operationalized into a functional, accurate, and trustworthy Retrieval-Augmented Generation (RAG) prototype for assisting healthcare professionals in Clinical Decision Support. While Large Language Models (LLMs) are powerful in knowledge synthesis, they often lack transparency and produce hallucinations. RAG can help address the problem by mitigating LLM hallucinations by grounding outputs in verified medical literature and clinical guidelines. This project bridges the clinical trust gap by implementing five core design principles into a RAG prototype that grounds AI responses in verified medical literature.

Publication Date

2026

Recommended Citation

Pecherskaia, Irina; Mixon, Jason; Behrens, Andrew; and Smith, Andrew, "Building Explainable RAG-based Clinical Decision Support" (2026). Annual Research Symposium. 73.
https://scholar.dsu.edu/research-symposium/73