This research introduces a novel conformal prediction framework for medical multiple-choice question answering that provides statistical guarantees of correctness.

• Addresses LLM hallucination problems in high-stakes medical applications
• Creates prediction sets that contain the correct answer with a guaranteed probability
• Demonstrates effectiveness across medical MCQA datasets
• Enhances trustworthiness without sacrificing performance

This breakthrough matters for medical applications by providing statistical rigor to AI-assisted decision making, potentially enabling safer deployment of LLMs in clinical settings where accuracy is critical.

Statistical Guarantees of Correctness Coverage for Medical Multiple-Choice Question Answering