Comparison of reference management software with new artificial intelligence-based tools
- Author:
Jae Gyeong JIN
1
;
Seung Gyu LEE
;
Jea Hyeun PARK
;
Jang Won HAN
;
Jae Young KIM
;
Jungirl SEOK
;
Jeong-Ju YOO
Author Information
- Publication Type:Software report
- From:Journal of Educational Evaluation for Health Professions 2026;23(1):2-
- CountryRepublic of Korea
- Language:0
- Abstract: Reference management software (RMS) represents a cornerstone of modern academic writing and publishing. For decades, programs such as EndNote, Zotero, and Mendeley have played central roles in facilitating citation organization, bibliography formatting, and collaborative scholarship. Although each platform has introduced unique innovations, persistent limitations remain, particularly with respect to usability, accessibility, and accuracy. In parallel, the rise of generative artificial intelligence has introduced an unprecedented challenge: the inadvertent inclusion of fabricated or incorrect references mistakenly incorporated into manuscripts. This phenomenon has exposed a critical limitation of traditional RMS platforms, namely their inability to verify reference authenticity. Against this backdrop, new solutions have emerged. One such example is CiteWell (https://citewell.org/), an artificial intelligence (AI)-era RMS that introduces several notable innovations, including PubMed-integrated verification, an intuitive interface for new users, customizable journal-specific styles, and multilingual accessibility. This review provides a comprehensive historical overview of RMS, evaluates the strengths and weaknesses of major platforms, and positions emerging AI-based tools as a new paradigm that combines traditional reference management with essential safeguards for contemporary academic challenges.
