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Enhancing quotation accuracy assessment with Chatpdf - a game-changer for a century-old conundrum
Muhammad Talal Ibrahim1, Cole Vincent Veliky2, Syeda Sadia Fatima3
1Section of Orthopedics, Department of Surgery, Aga Khan University Hospital.
JPMA. the Journal of the Pakistan Medical Association
|June 27, 2026
Summary
Artificial intelligence (AI) tools like ChatPDF can significantly improve the detection of quotation errors in medical literature, enhancing research reliability and saving valuable time for researchers.
Area of Science:
- Medical Informatics
- Bibliometrics
- Artificial Intelligence in Healthcare
Background:
- Quotation errors in medical literature undermine its reliability.
- Manual detection of these errors is a time-consuming process.
Purpose of the Study:
- To evaluate the accuracy and speed of ChatPDF, an AI-powered program, in identifying quotation errors.
- To assess the potential of AI in revolutionizing the assessment of quotation accuracy in scientific publications.
Main Methods:
- An observational cross-sectional study was conducted.
- 398 quotations from medical literature were assessed using the ChatPDF program.
- The program's performance in highlighting errors, providing answers, and overall helpfulness was recorded.
Main Results:
- Out of 398 quotations, 88 (22.1%) contained errors.
- ChatPDF showed higher accuracy and helpfulness in detecting errors in correctly supported quotations (e.g., 84.5% comprehensive answers) compared to erroneous ones (e.g., 61.4% comprehensive answers).
- A statistically significant difference (p<0.001) was observed in ChatPDF's performance between supported and erroneous quotations.
Conclusions:
- ChatPDF demonstrates significant potential for efficiently and accurately detecting quotation errors in medical literature.
- AI-driven tools can streamline the quality control process for scientific publications.
- Implementing AI can enhance the overall reliability of published medical research.
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