Distinguishing GPT-4-generated Radiology Abstracts from Original Abstracts: Performance of Blinded Human Observers and AI Content Detector

Abstract

Abstract Objective To determine GPT-4’s effectiveness in writing scientific radiology article abstracts and investigate human reviewers’ and AI Content detectors’ success in distinguishing these abstracts. Additionally, to determine the similarity scores of abstracts generated by GPT-4 to better understand its ability to create unique text. Methods The study collected 250 original articles published between 2021 and 2023 in five radiology journals. The articles were randomly selected, and their abstracts were generated by GPT-4 using a specific prompt. Three experienced academic radiologists independently evaluated the GPT-4 generated and original abstracts to distinguish them as original or generated by GPT-4. All abstracts were also uploaded to an AI Content Detector and plagiarism detector to calculate similarity scores. Statistical analysis was performed to determine discrimination performance and similarity scores. Results Out of 134 GPT-4 generated abstracts, average of 75 (56%) were detected by reviewers, and average of 50 (43%) original abstracts were falsely categorized as GPT-4 generated abstracts by reviewers. The sensitivity, specificity, accuracy, PPV, and NPV of observers in distinguishing GPT-4 written abstracts ranged from 51.5% to 55.6%, 56.1% to 70%, 54.8% to 60.8%, 41.2% to 76.7%, and 47% to 62.7%, respectively. No significant difference was observed between observers in discrimination performance. Conclusion GPT-4 can generate convincing scientific radiology article abstracts. However, human reviewers and AI Content detectors have difficulty in distinguishing GPT-4 generated abstracts from original ones.

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