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

doi:10.47391/JPMA.AKU-10Surg-28

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

Research output: Contribution to journal › Article › peer-review

Abstract

Quotation errors compromise the reliability of published medical literature, but assessing these errors is very time-intensive. 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.

Original language	English (US)
Pages (from-to)	S150-S153
Journal	Journal of the Pakistan Medical Association
Volume	76
Issue number	5
DOIs	https://doi.org/10.47391/JPMA.AKU-10Surg-28
Publication status	Published - 19 May 2026

Keywords

Artificial intelligence
ChatPDF
Natural language processing
Quotation error
Referencing error

Access to Document

10.47391/JPMA.AKU-10Surg-28

Cite this

@article{508942ffb9a94e30a81f74ecaf12f17c,

title = "Enhancing quotation accuracy assessment with Chatpdf – a game-changer for a century-old conundrum",

abstract = "Quotation errors compromise the reliability of published medical literature, but assessing these errors is very time-intensive. 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.",

keywords = "Artificial intelligence, ChatPDF, Natural language processing, Quotation error, Referencing error",

author = "Ibrahim, \{Muhammad Talal\} and Veliky, \{Cole Vincent\} and Fatima, \{Syeda Sadia\} and Zahra Hoodbhoy and Shahryar Noordin",

note = "Publisher Copyright: Copyright (c) 2026 Journal of the Pakistan Medical Association. This work is licensed under a Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/.",

year = "2026",

month = may,

day = "19",

doi = "10.47391/JPMA.AKU-10Surg-28",

language = "English (US)",

volume = "76",

pages = "S150--S153",

journal = "Journal of the Pakistan Medical Association",

issn = "0030-9982",

publisher = "Pakistan Medical Association",

number = "5",

}

TY - JOUR

T1 - Enhancing quotation accuracy assessment with Chatpdf – a game-changer for a century-old conundrum

AU - Ibrahim, Muhammad Talal

AU - Veliky, Cole Vincent

AU - Fatima, Syeda Sadia

AU - Hoodbhoy, Zahra

AU - Noordin, Shahryar

N1 - Publisher Copyright: Copyright (c) 2026 Journal of the Pakistan Medical Association. This work is licensed under a Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/.

PY - 2026/5/19

Y1 - 2026/5/19

N2 - Quotation errors compromise the reliability of published medical literature, but assessing these errors is very time-intensive. 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.

AB - Quotation errors compromise the reliability of published medical literature, but assessing these errors is very time-intensive. 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.

KW - Artificial intelligence

KW - ChatPDF

KW - Natural language processing

KW - Quotation error

KW - Referencing error

UR - https://www.scopus.com/pages/publications/105043875357

U2 - 10.47391/JPMA.AKU-10Surg-28

DO - 10.47391/JPMA.AKU-10Surg-28

M3 - Article

C2 - 42363395

AN - SCOPUS:105043875357

SN - 0030-9982

VL - 76

SP - S150-S153

JO - Journal of the Pakistan Medical Association

JF - Journal of the Pakistan Medical Association

IS - 5

ER -

Enhancing quotation accuracy assessment with Chatpdf – a game-changer for a century-old conundrum

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this