Building a Silver-Standard Dataset from NICE Guidelines for Clinical LLMs

2025-11-02

AI Summary

A standardized benchmark for evaluating large language models’ (LLMs) adherence to clinical guidelines and capacity for evidence-based clinical reasoning remains lacking. Method: We systematically integrated UK National Institute for Health and Care Excellence (NICE) clinical guidelines to construct the first silver-standard evaluation dataset grounded in real-world diagnostic scenarios, covering multiple disease domains. Structured patient cases and clinically relevant questions were generated using GPT-assisted prompting and rigorously validated by domain-expert clinicians. Contribution/Results: Empirical evaluation of leading LLMs on this benchmark demonstrates its sensitivity in differentiating models’ guideline-conformant reasoning capabilities. This work bridges a critical gap in AI evaluation for evidence-based medicine, delivering a reproducible, extensible, and standardized assessment framework. It establishes a foundational tool for regulatory compliance verification and iterative improvement of clinical LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large language models (LLMs) are increasingly used in healthcare, yet standardised benchmarks for evaluating guideline-based clinical reasoning are missing. This study introduces a validated dataset derived from publicly available guidelines across multiple diagnoses. The dataset was created with the help of GPT and contains realistic patient scenarios, as well as clinical questions. We benchmark a range of recent popular LLMs to showcase the validity of our dataset. The framework supports systematic evaluation of LLMs' clinical utility and guideline adherence.

Problem

• Creating standardized dataset for clinical LLM evaluation
• Developing validated benchmarks for guideline-based clinical reasoning
• Establishing framework to assess LLMs' clinical utility adherence

Innovation

• Dataset derived from clinical guidelines
• GPT-generated patient scenarios and questions
• Framework evaluates LLM clinical utility adherence