Reader's digest version of scientific writing: comparative evaluation of summarization capacity between large language models and medical students in analyzing scientific writing in sleep medicine.

Journal: Frontiers in artificial intelligence
Published Date: Dec 24, 2024

Abstract

INTRODUCTION: As artificial intelligence systems like large language models (LLM) and natural language processing advance, the need to evaluate their utility within medicine and medical education grows. As medical research publications continue to grow exponentially, AI systems offer valuable opportunities to condense and synthesize information, especially in underrepresented areas such as Sleep Medicine. The present study aims to compare summarization capacity between LLM generated summaries of sleep medicine research article abstracts, to summaries generated by Medical Student (humans) and to evaluate if the research content, and literary readability summarized is retained comparably.

Authors

  • Jacob Matalon
    Medical school, California University of Science and Medicine, Colton, CA, United States.
  • August Spurzem
    Medical school, California University of Science and Medicine, Colton, CA, United States.
  • Sana Ahsan
    Medical school, California University of Science and Medicine, Colton, CA, United States.
  • Elizabeth White
    Medical school, California University of Science and Medicine, Colton, CA, United States.
  • Ronik Kothari
    Medical school, California University of Science and Medicine, Colton, CA, United States.
  • Madhu Varma
    Department of Medical Education and Clinical Skills, California University of Science and Medicine, Colton, CA, United States.

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