What Do Editors-in-Chief of Medical Journals Think About the Use of Artificial Intelligence Chatbots in the Scholarly Publishing Process? Results From An International Cross-Sectional Survey Across Multiple Publishers
Notice bibliographique
Résumé
Objective: This study aimed to examine the attitudes and perceptions of Editors-in-Chief (EiCs) of biomedical journals regarding the integration of artificial intelligence chatbots (AICs) into the scholarly publishing process. While AICs offer opportunities to streamline editorial tasks such as plagiarism detection, language editing, and ethics screening, they also introduce ethical, technical, and operational challenges. Understanding EiC perspectives is critical to shaping guidelines, policies, and training programs that align with the evolving role of AICs in scientific publishing. Design: We conducted a cross-sectional survey of EiCs from biomedical journals published by Springer & BMC (part of Springer Nature), Taylor & Francis, Elsevier, Wiley, and SAGE, which are the five largest academic publishers by journal count. Eligible journals were identified through a combination of automated web scraping of publisher webpages and manual verification. A total of 3381 EiCs were invited via email to participate in an anonymous online survey conducted over five weeks in 2024, which included three follow-up reminders. The survey covered familiarity with AICs, current usage, perceived benefits and challenges, and anticipated future roles. Quantitative data were analyzed using descriptive statistics, while qualitative responses underwent thematic content analysis to identify key themes. Results: Of the 3381 EiCs contacted, 510 responded (15.1% response rate), with 505 eligible participants and a completion rate of 87.0%. Most respondents were familiar with AICs (66.7%, 325/487) but had not used them in editorial workflows (83.7%, 401/479). Perceived benefits included enhanced language and grammar support (70.8%, 308/435) and plagiarism screening (67.3%, 294/437). However, respondents expressed concerns about initial setup and training (83.9%, 360/429), ethical risks (80.6%, 345/428), and technical reliability (75.2%, 322/428). While only 49.6% (240/484) of journals reported having formal AIC policies, 89.5% (419/468) of respondents supported training initiatives to promote ethical and effective usage. Despite limited current adoption, 78.9% (370/469) believed AICs will play an important role in the future of scholarly publishing, and 77.2% (363/470) anticipated their significance in advancing scientific research. Themes identified through thematic analysis of open-ended questions include: “no AI in authorship or peer review” referring to the EiC current journal/publisher policy on AIC use, and “ethical, integrity, and privacy concerns” referring to EiC perceptions of challenges with the use of AICs in the scholarly publishing process. Conclusions: Biomedical journal EiCs recognize AICs’ potential to enhance editorial processes but highlight critical barriers, including ethical dilemmas, resource limitations, and insufficient policies and training. Structured interventions, including targeted training programs and robust ethical guidelines, are essential for addressing these challenges and ensuring responsible and effective integration of AICs into publishing workflows.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,130 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».