SurgiQ: New Benchmark Tests Large Language Models on Surgical Knowledge and Reasoning
Researchers introduced SurgiQ, a large-scale benchmark with over 13,000 multiple-choice questions designed to evaluate how well large language models understand surgical concepts and decision-making. The benchmark spans six surgical domains and tests procedural reasoning, trade-offs, and negation handling—capabilities beyond general medical knowledge. The findings reveal significant gaps in current models, with the best performing at 68.1% accuracy and general-purpose models outperforming specialized medical models.
SurgiQ is a text-based evaluation benchmark containing 13,055 four-option multiple-choice questions sourced from surgical textbooks, open-access papers, and examination materials. The benchmark was constructed through a multi-stage pipeline involving generation, verification, and expert auditing, covering six surgical domains and four question formats: case-based, reasoning, best-option, and negative. Evaluation of 35 open-weight large language models revealed substantial performance gaps, with smaller models often performing near the 25% random baseline and the best model reaching 68.1% accuracy. Notably, general-purpose models like Qwen2.5 outperformed most biomedical-specialized models, suggesting that current medical specialization strategies have not yet achieved sufficiently broad surgical coverage. Error analysis showed that even strong models make confident mistakes on clinically plausible distractors, highlighting the need for more reliable surgical LLM evaluation methods.
What's missing
The study does not discuss potential limitations of the benchmark itself, such as whether multiple-choice format adequately captures real surgical decision-making, how the benchmark might evolve as models improve, or specific recommendations for improving model performance on surgical tasks.
What different sources said
- arXiv cs.AICenter
MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science
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