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Related Concept Videos

Assessment of the Rectum and Anus01:25

Assessment of the Rectum and Anus

Evaluating the rectum and anus plays a crucial role in conducting a thorough physical examination of the gastrointestinal system. Although it may be uncomfortable and often embarrassing for the patient, it holds immense diagnostic value, particularly in detecting gastrointestinal diseases and abnormalities. This guide will explain how to perform this assessment using inspection and palpation methods.
Rectal Inspection
Begin by inspecting the perianal and anal areas for color, texture, rashes,...

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Artificial intelligence performance in generating colorectal surgery board questions.

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Large language models (LLMs) show potential but cannot yet reliably create high-quality colorectal surgery board exam questions. Expert-developed questions significantly outperform LLM-generated content in board-style assessments.

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Medicine
  • Surgical Training

Background:

  • Large language models (LLMs) demonstrate proficiency in passing medical licensing exams.
  • The capability of LLMs to generate high-quality, board-style medical exam questions remains largely unexamined.
  • Assessing LLM-generated questions is crucial for their potential integration into medical education.

Purpose of the Study:

  • To evaluate the quality of colorectal surgery board-style exam questions generated by three leading LLMs.
  • To compare LLM-generated questions against established educational materials.
  • To determine the suitability of LLM-generated questions for board certification preparation.

Main Methods:

  • Three LLMs generated 20 colorectal surgery board questions each, adhering to American Board of Colon and Rectal Surgery guidelines.
  • Questions from the Colon and Rectal Surgery Educational Program (CARSEP) were used as a benchmark.
  • Board-certified colorectal surgeons evaluated questions based on clarity, relevance, suitability, distractor quality, and rationale, categorizing them as 'Approved for Committee,' 'Author to Review,' or 'Not Accepted.'

Main Results:

  • CARSEP questions achieved the highest 'Approved for Committee' rate (65%), significantly outperforming LLMs (ChatGPT-4o: 7%, Copilot Pro: 10%, Gemini Advanced: 10%).
  • LLMs were significantly outperformed by CARSEP across most evaluation domains (p < 0.001).
  • LLM-generated questions received high ratings (>70%) for suitability, indicating potential in specific aspects.

Conclusions:

  • Current LLMs are not consistently capable of generating high-quality, board-style colorectal surgery exam questions.
  • LLMs show promise but require further development to meet the rigorous standards of surgical board examinations.
  • Expert-developed question banks remain superior for board preparation and assessment.