Work

DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening

Year: 2026

Type: conference-paper

Abstract: Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and citation errors.We present DI-ASENTINEL, a fully on-premise multi-agent system for one-year type 2 diabetes mellitus (T2DM) risk screening and guideline-grounded report generation from electronic health records (EHRs).The system integrates calibrated risk prediction, deterministic clinical signal extraction, Reciprocal Rank Fusion over American Diabetes Association (ADA) guidelines, and a hybrid verification layer combining rule-based checks with LLM entailment.The demonstration provides a real-time batch-screening dashboard and an interactive patient report interface with cited recommendations, verification results, and raw EHR comparison.DIASEN-TINEL demonstrates a practical framework for reliable, auditable, and privacy-preserving LLM-based clinical decision support.Our system demonstration can be found at https: //diasentinel-demo.onrender.com/

Source: Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP)

DOI: 10.18653/2026.emnlp-demos.32