Enhancing quotation accuracy assessment with Chatpdf – a game-changer for a century-old conundrum
DOI:
https://doi.org/10.47391/JPMA.AKU-10Surg-28Abstract
Quotation errors compromise the reliability of published medical literature, but assessing these errors is very timeintensive. The current observational cross-sectional study was planned to assess the accuracy and speed of detecting quotation errors using the ChatPDF programme that is powered by artificial intelligence. Of the 398 quotations assessed, 310(77.9%) were fully supported, while 88(22.1%) had errors. Among the former quotations, the ChatPDF programme could comprehensively highlight 210(67.7%) PDFs, provided comprehensive answers in 262(84.5%), and was completely helpful in 248(80%) cases. In contrast, for erroneous quotations, the corresponding values were 36(40.9%), 54(61.4%) and 43(48.9%), respectively (p<0.001). ChatPDF was found to have immense potential to revolutionise quotation error assessments.
Keyword: Artificial intelligence, Natural language processing, ChatPDF, Referencing error, Quotation error.
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