Evaluating the Efficacy of Perplexity Scores in Distinguishing AI-Generated and Human-Written Abstracts

We aimed to evaluate the efficacy of perplexity scores in distinguishing between human-written and AI-generated radiology abstracts and to assess the relative performance of available AI detection tools in detecting AI-generated content. Academic articles were curated from PubMed using the keywords "neuroimaging" and "angiography." Filters included English-language, open-access articles with abstracts without subheadings, published before 2021, and within Chatbot processing word limits. The first 50 qualifying articles were selected, and their full texts were used to create AI-generated abstracts. Perplexity scores, which estimate sentence predictability, were calculated for both AI-generated and human-written abstracts. The performance of three AI tools in discriminating human-written from AI-generated abstracts was assessed. The selected 50 articles consist of 22 review articles (44%), 12 case or technical reports (24%), 15 research articles (30%), and one editorial (2%). The perplexity scores for human-written abstracts (median; 35.9 IQR; 25.11–51.8) were higher than those for AI-generated abstracts (median; 21.2 IQR; 16.87–28.38), (p = 0.057) with an AUC = 0.7794. One AI tool performed less than chance in identifying human-written from AI-generated abstracts with an accuracy of 36% (p > 0.05) while another tool yielded an accuracy of 95% with an AUC = 0.8688. This study underscores the potential of perplexity scores in detecting AI-generated and potentially fraudulent abstracts. However, more research is needed to further explore these findings and their implications for the use of AI in academic writing. Future studies could also investigate other metrics or methods for distinguishing between human-written and AI-generated texts.