Evaluating Incontinence Abstracts: Artificial Intelligence-Generated Versus Cochrane Review

Abstract

IMPORTANCE: As the volume of medical literature continues to expand, the provision of artificial intelligence (AI) to produce concise, accessible summaries has the potential to enhance the efficacy of content review. OBJECTIVES: This project assessed the readability and quality of summaries generated by ChatGPT in comparison to the Plain Text Summaries from Cochrane Review, a systematic review database, in incontinence research. STUDY DESIGN: Seventy-three abstracts from the Cochrane Library tagged under "Incontinence" were summarized using ChatGPT-3.5 (July 2023 Version) and compared with their corresponding Cochrane Plain Text Summaries. Readability was assessed using Flesch Kincaid Reading Ease, Flesch Kincaid Grade Level, Gunning Fog Score, Smog Index, Coleman Liau Index, and Automated Readability Index. A 2-tailed t test was used to compare the summaries. Each summary was also evaluated by 2 blinded, independent reviewers on a 5-point scale where higher scores indicated greater accuracy and adherence to the abstract. RESULTS: Compared to ChatGPT, Cochrane Review's Plain Text Summaries scored higher in the numerical Flesch Kincaid Reading Ease score and showed lower necessary education levels in the 5 other readability metrics with statistical significance, indicating better readability. However, ChatGPT earned a higher mean accuracy grade of 4.25 compared to Cochrane Review's mean grade of 4.05 with statistical significance. CONCLUSIONS: Cochrane Review's Plain Text Summaries provide clearer summaries of the incontinence literature when compared to ChatGPT, yet ChatGPT generated more comprehensive summaries. While ChatGPT can effectively summarize the medical literature, further studies can improve reader accessibility to these summaries.

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