This is like giving doctors a very smart assistant that has read all the European Society of Cardiology (ESC) guidelines and can instantly explain what they mean for a specific patient, instead of the doctor manually searching long PDF documents.
Clinicians struggle to quickly find and interpret the right recommendation in dense ESC cardiology guidelines for a specific patient scenario. This work evaluates whether large language models (LLMs) can accurately interpret and apply these guidelines, potentially reducing time spent searching, lowering cognitive load, and standardizing adherence to evidence-based care.
Tight integration with ESC guideline content and cardiology workflows, plus clinically validated evaluation methodology and benchmarks for LLM performance in guideline interpretation.
Frontier Wrapper (GPT-4)
Vector Search
Medium (Integration logic)
Context window cost and need for tight safety/validation loops to avoid hallucinated or unsafe clinical recommendations.
Early Adopters
Narrow focus on ESC cardiology guideline interpretation with systematic accuracy and applicability evaluation, rather than generic ‘AI for healthcare’ claims.
2 use cases in this application