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) benchmark showed that direct zero-shot large language model (LLM) generation is poorly suited to this task: clinical code systems are large, version-controlled, and not reliably memorized by language models. We study a stage-wise alternative in which candidate-pool construction is optimized for recall and a constrained LLM adj
The proliferation of LLMs creates an immediate need for robust methods to apply them to structured, high-stakes domains like clinical data authoring, which prior models have struggled with.
This research provides a concrete approach for making LLMs reliable and useful in critical applications, directly addressing a key limitation in their current deployment.
The ability to use AI for high-quality, retrieval-constrained clinical value set authoring moves LLMs beyond 'zero-shot' generalisation to practical, domain-specific utility.
- · Healthcare AI Developers
- · Clinical Research Organizations
- · LLM Providers
- · Healthcare Systems
- · Manual Clinical Coders (long-term)
- · Legacy Clinical Data Management Solutions
Improved accuracy and efficiency in authoring clinical value sets using AI.
Faster development and deployment of AI-driven tools in medical diagnostics and treatment planning.
Enhanced precision medicine capabilities and reduced human error in clinical data interpretation at scale.
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Read at arXiv cs.AI