DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening
cs.CL
Submitted: 2026-08-31
Updated: 2026-08-31
License: http://creativecommons.org/licenses/by/4.0/
The gist: Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and citation errors.
Terminology
Abstract
Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and citation errors. We present DIASENTINEL, 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. DIASENTINEL demonstrates a practical framework for reliable, auditable, and privacy-preserving LLM-based clinical decision support.
Sources
- Docling Technical Report
- Medical Hallucinations in Foundation Models and Their Impact on Healthcare
- M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
- Qwen2.5 Technical Report
- Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAI
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