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Using Large Language Models to Summarize Evidence in Biomedical Articles: Exploratory Comparison Between AI- and

Michelle Colder Carras1, Riaz Qureshi2, Kevin Naaman2

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Summary
This summary is machine-generated.

AI-generated annotations using ChatGPT can provide a rapid overview of scientific literature, offering consistent quality and context but with potential inaccuracies and more errors than human annotations. Careful human verification is recommended for AI-generated annotated bibliographies.

Keywords:
ChatGPTannotated bibliographyartificial intelligenceevidence synthesisinformation managementlarge language model

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Scientific Communication

Background:

  • Annotated bibliographies are crucial for literature review but require significant human expertise and time.
  • AI tools offer potential for efficient summarization, yet may introduce errors.

Purpose of the Study:

  • To evaluate the feasibility of using ChatGPT for generating annotated bibliographies.
  • To compare AI-generated annotations with human-written ones regarding accuracy, conciseness, and quality.

Main Methods:

  • Two human annotators and three versions of ChatGPT (3.5, 4, 5) independently annotated 15 publications.
  • Annotations were assessed for word count, readability (Flesch Reading Ease), capture of main points, presence of errors, and inclusion of quality/context.
  • Statistical models were used to compare annotations generated by humans and AI.

Main Results:

  • ChatGPT annotations were longer and less readable than human annotations.
  • No significant difference was found in capturing main points between AI and human annotations.
  • Human annotations had fewer errors, while AI annotations more consistently included quality and context, though sometimes inaccurately.

Conclusions:

  • AI tools like ChatGPT can efficiently generate literature summaries, providing consistent quality and context.
  • AI-generated annotations may contain more errors and inaccuracies, necessitating human verification.
  • Further research into prompt engineering is needed to enhance AI chatbot performance for scientific literature summarization.