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

arXiv: https://arxiv.org/abs/2511.01053 · HTML: https://mlsanity.com/paper/2511.01053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 2 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes](https://mlsanity.com/paper/2309.00237/) (2023)
- [Clean & Clear: Feasibility of Safe LLM Clinical Guidance](https://mlsanity.com/paper/2503.20953/) (2025)

## Built on by (most cited first, 1 of 1)

- [SafeLLM: Extraction as a Hallucination-Resistant Alternative to Rewriting in Safety-Critical Settings](https://mlsanity.com/paper/2606.12897/) (2026)

## Abstract (arXiv)

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.