Benchmarking Large Language AI Models Against Human Doctors Using an Emergency Medicine Simulation Approach

Background: Proper adoption of AI in emergency medicine necessitate thorough and rigorous appraisal. Classic benchmarks, which rely heavily on basic question-and-answer formats, though dominating the benchmarking field of LLM usage in medicine, have proven inadequate in assessing the full spectrum of skills incumbent upon an agent in healthcare. Methods: We propose an intricate benchmarking scheme in which the skill set required of an emergency physician is tested and quantified. Our benchmarking structure is 3-fold: skills such as history taking, diagnostic accuracy, treatment plan, and empat

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