arXiv AI By Ashvin Gupta, Denys Prociuk, Alessandra Russo, Brendan C. Delaney

Automatic Conversion of NICE Guidelines to an Executable Computational Model Using Large Language Models

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arXiv AI
Aug 28

Evaluating AI Generated Summaries for Cancer Patients

The study evaluates AI-generated summaries for cancer patients using a dual assessment framework that includes human experts and LLM-as-a-judge. Human domain experts—oncology clinicians and patient-facing care staff—assess summary quality on accuracy, clinical relevance, and readability. The research identifies limitations such as omissions and minor inaccuracies, which are then used to iteratively refine prompts, grounding, and safety guardrails.

By Muhammad Aurangzeb Ahmad, Kim Shyu, Leon Oliver, Fergus Sleight, Paul Landau
arXiv Computation and Language
Aug 27

Retrieval-Augmented Agentic Rubric Generation for Reliable Medical Response Evaluation

The paper introduces a retrieval‑augmented multi‑agent framework that automatically generates instance‑specific evaluation rubrics for medical language models. By retrieving authoritative medical evidence, decomposing it into atomic facts, and combining these with user interaction constraints, the system produces fine‑grained criteria that outperform GPT‑4o on HealthBench and LLMEval‑Med. The generated rubrics also guide response refinement, improving medical LLM output quality by 9.2%.

By Yinzhu Chen, Abdine Maiga, Hossein A. Rahmani, Emine Yilmaz