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Area of Science:

  • Artificial Intelligence in Scientific Writing
  • Radiology Editorial Content Generation
  • Natural Language Processing in Academia

Background:

  • Artificial intelligence (AI) models like GPT-4 can produce human-like text.
  • The capability of AI to write scientific content requires evaluation for its real-world applicability.

Purpose of the Study:

  • To assess GPT-4's ability to generate radiology editorials.
  • To compare AI-generated editorials with human-written ones.
  • To determine the detectability and publishability of AI-generated scientific content.

Main Methods:

  • GPT-4 generated 16 editorials based on human-written summaries.
  • Six experienced editors evaluated articles using Likert scales and authorship determination (unpaired and paired settings).
  • Articles were analyzed using AI detection and plagiarism software.

Main Results:

  • AI-written articles had a median AI probability score of 58%, compared to 2.0% for human-written articles.
  • Authorship classification accuracy reached 70% in the paired setting.
  • Editors preferred publishing human-written articles (82%) and rated them higher when perceived as human-authored.

Conclusions:

  • GPT-4 produces high-quality, plagiarism-free editorials that are difficult to detect with current tools.
  • AI-generated content may be published but faces editor bias towards human-written work.
  • Detection tools have limited effectiveness in identifying AI-generated scientific text.