mlsanityData to 14 Oct 2026

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

arXiv 2608.31128 ↗First posted 31 Aug 2026cs.CLcited by 0

On the map: LLM agents and tool use → Medical and scientific domain agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 3 of its 17 references that are in the map. First posted on arXiv 31 Aug 2026.

Builds on

3 of 17 references.

References · 3

  1. 2017On Calibration of Modern Neural Networkscited by 4,960
  2. 2025Medical Hallucinations in Foundation Models and Their Impact on Healthcarecited by 93
  3. 2024Exploration of LLM Multi-Agent Application Implementation Based on LangGraph+CrewAIcited by 35

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