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Artificial Intelligence-Generated Editorials in Radiology: Can Expert Editors Detect Them?

Burak Berksu Ozkara¹, Alexandre Boutet², Bryan A Comstock³

¹From the Department of Neuroradiology (B.B.O., M.W.), The University of Texas MD Anderson Center, Houston, Texas.

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|September 17, 2024

Summary

This summary is machine-generated.

GPT-4 can generate high-quality radiology editorials that may evade detection. However, editors showed a preference for human-written content, indicating a bias in scientific writing evaluation.

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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.