EHRBench uses an EHR-LLM-KB pipeline to automatically create 960,067 reliable QA items spanning diagnosis, treatment, and prognosis for large-scale LLM evaluation in clinical decision making.
Dorfner, Amin Dada, Felix Busch, Mar- cus R
3 Pith papers cite this work. Polarity classification is still indexing.
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A new 40,857-sample multilingual multimodal medical QA benchmark (ArogyaBodha) and an actor-critic agent framework (ArogyaSutra) that improve Indic-language medical reasoning accuracy by up to 9.2 points over its base model.
Mistral uses careful lexical simplification to raise readability while keeping BERTScore at 0.91 comparable to humans, whereas QWen improves readability but shows a disconnect with its 0.89 BERTScore in biomedical text simplification.
citing papers explorer
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EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs
EHRBench uses an EHR-LLM-KB pipeline to automatically create 960,067 reliable QA items spanning diagnosis, treatment, and prognosis for large-scale LLM evaluation in clinical decision making.
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ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages
A new 40,857-sample multilingual multimodal medical QA benchmark (ArogyaBodha) and an actor-critic agent framework (ArogyaSutra) that improve Indic-language medical reasoning accuracy by up to 9.2 points over its base model.
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Making Knowledge Accessible: Divergent Readability-Accuracy Strategies of Mistral and QWen in Biomedical Text Simplification
Mistral uses careful lexical simplification to raise readability while keeping BERTScore at 0.91 comparable to humans, whereas QWen improves readability but shows a disconnect with its 0.89 BERTScore in biomedical text simplification.