T2D-Bench: Evidence-Gated Evaluation of LLM Outputs for Type 2 Diabetes Using a Multi-Layer Clinical-Lifestyle Knowledge Graph
arXiv:2606.24145v1 Announce Type: new Abstract: Large language models (LLMs) can produce clinically fluent recommendations for type 2 diabetes while failing to satisfy guideline constraints or explicitly justify lifestyle-related glycemic claims. We present T2D-Bench, a reproducible benchmark and evidence-gated evaluation framework for testing whether LLM outputs satisfy explicit, graph-checkable evidence requirements. T2D-Bench is built on a multi-layer clinical-lifestyle knowledge graph that combines a biomedical spine (UMLS, DrugBank, SIDER), computable ADA Standards of Care rules, and life
The proliferation of LLMs in critical domains like healthcare necessitates robust evaluation frameworks to address their inherent limitations and ensure safe, reliable deployment.
This development allows for evidence-gated evaluation of LLMs, moving beyond mere fluency to verifiable clinical accuracy, which is crucial for trust and adoption in sensitive fields.
The ability to systematically benchmark and evaluate LLM outputs against concrete, graph-checkable evidence rules changes how AI models will be validated and deployed in expert domains, especially healthcare.
- · AI safety researchers
- · Healthcare providers
- · AI developers focused on accuracy and trust
- · Patients with chronic conditions
- · LLM developers prioritizing fluency over factual accuracy
- · Regulatory bodies without rigorous evaluation methods
Increased trust and adoption of LLM-powered advisory systems in healthcare, starting with chronic disease management.
Demand for similar evidence-gated evaluation frameworks in other high-stakes domains like law, finance, and engineering.
The development of a new industry standard for 'fact-checked AI' where all advisory LLMs must pass an evidence-gated certification.
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Read at arXiv cs.AI