New benchmark tests LLMs on multi-turn clinical question answering

Researchers have introduced EHRNote-ChatQA, a novel benchmark designed to evaluate multi-turn clinical question answering over longitudinal patient discharge summaries. This benchmark, derived from de-identified MIMIC-IV data, features over 16,000 expert-verified question-answer pairs across 967 patient-level samples. Initial evaluations of 22 LLMs indicate significant challenges in evidence grounding and error compounding across multiple turns, suggesting that performance on single-turn clinical QA does not reliably translate to this more complex setting. AI

IMPACT Establishes a new evaluation standard for clinical LLM applications, highlighting current limitations in evidence grounding and multi-turn reasoning.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• EHRNote-ChatQA
• MIMIC-IV
• PhysioNet

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →