Enhancing quotation accuracy assessment with Chatpdf – a game-changer for a century-old conundrum - Muhammad Talal Ibrahim; Cole Vincent Veliky; Syeda Sadia Fatima; Zahra Hoodbhoy; Shahryar Noordin (Journal of the Pakistan Medical Association, 2026)
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.