Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Perspectives on Artificial Intelligence in Medical Publishing: A Survey of Medical Journal Editors
0
Zitationen
34
Autoren
2025
Jahr
Abstract
Artificial intelligence (AI) has been increasingly integrated into medical publishing, hopefully improving efficiency and accuracy, but serious concerns persist regarding ethical implications, authorship attribution, and content reliability. We aimed at understanding the perspectives of editors of medical journals on AI. A structured online questionnaire was developed and distributed to editors-in-chief of medical journals worldwide. The survey comprised 27 concise questions exploring demographics, journal practices, and perspectives on AI in editorial workflows. Quantitative data were analyzed using descriptive statistics to summarize usage patterns, perceived benefits, risks, and future expectations. A total of 59 editors-in-chief completed the survey (response rate: 19%), with replies suggesting substantial variability in beliefs and attitudes toward AI for publication in medical journals. Artificial intelligence tools were already in use by 49% of journals, mainly for plagiarism detection (76%) and data verification (35%). Only 9% of responders reported that journals used AI for both scientific and linguistic review. Time savings (79%) and cost reduction (43%) were the most commonly cited benefits, and concerns included potential bias (71%) and lack of accountability (60%). Overall, 81% of responders anticipated a major role for AI in publishing within 10 years. Exploratory analyses suggested several potential associations between replies and respondent or journal features, requiring further validation in future surveys. In conclusion, this survey on attitudes toward AI in publication in medical journals suggests that editors-in-chief are cautiously adopting AI in their editorial workflow, supporting its operational use while explicitly calling for clear guidance to address ethical and regulatory concerns.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.260 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.116 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.493 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.438 Zit.
Autoren
- Giuseppe Biondi-Zoccai
- Attilio Lauretti
- Stefan Agewall
- Emmanuel Andres
- Riccardo A. Audisio
- Deepak Bhatt
- Giuseppe Citerio
- Jonathan A. Drezner
- Alexander M.M. Eggermont
- Cetin Erol
- Karen D. Ersche
- Giorgio Ferriero
- Gerd Heusch
- ciro Indolfi
- Paul A. Insel
- Carl J. Lavie
- Carlo La Vecchia
- Nicola Maffulli
- Fabrizio Montecucco
- David J. Moliterno
- Stanley Nattel
- Amit Patel
- E Oliaro
- Antonio Pelliccia
- Michael Picard
- Paolo Pozzilli
- Fabiana Quaglia
- Renata L. Riha
- Rupa Sarkar
- Pietro Scicchitano
- et al.
- Artificial Intelligence in Medical Publishing (AIMPub) Working Group
- et al.
- Arnold Von Eckardstein