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LLM adjudication improves clinical value set authoring with RASC+ method

A new research paper introduces RASC+, a method for improving the authoring of clinical value sets using large language models (LLMs). The study found that a two-stage approach, where an initial retrieval system identifies candidate codes and a constrained LLM adjudicates the selection, significantly outperforms direct LLM generation. This method, tested on a large dataset, demonstrated a substantial increase in F1 scores, particularly when using GPT-5 for adjudication, while maintaining the crucial safety constraint of using auditable candidate pools. AI

IMPACT Enhances LLM capabilities in specialized domains like clinical terminology, potentially improving healthcare data standardization and analysis.

RANK_REASON The cluster contains a research paper detailing a new methodology for clinical value set authoring using LLMs.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM adjudication improves clinical value set authoring with RASC+ method

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The cluster contains a research paper detailing a new methodology for clinical value set authoring using LLMs.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Sumit Mukherjee ·

    RASC+: Retrieval-Constrained LLM Adjudication for Clinical Value Set Authoring

    arXiv:2606.23992v1 Announce Type: cross Abstract: Clinical value sets define the standardized terminology codes used in quality measurement, phenotyping, cohort construction, and clinical decision support. The recently introduced Retrieval-Augmented Set Completion (RASC) benchmar…

  2. arXiv cs.CL TIER_1 English(EN) · Sumit Mukherjee ·

    RASC+: Retrieval-Constrained LLM Adjudication for Clinical Value Set Authoring

    Clinical value sets define the standardized terminology codes used in quality measurement, phenotyping, cohort construction, and clinical decision support. The recently introduced Retrieval-Augmented Set Completion (RASC) benchmark showed that direct zero-shot large language mode…