Science Editing

Meeting Reports

Report on the Asia Pacific Association of Medical Journal Editors 2024 Conference: Young Yoo; Received September 16, 2024 Accepted September 26, 2024 Published online October 14, 2024; DOI: https://doi.org/10.6087/kcse.347 [Epub ahead of print]

442 View
8 Download

PDF

Artificial intelligence in scholarly publishing and the role of the Korean Association of Medical Journal Editors in the Asia-Pacific region: Young Yoo; Sci Ed. 2024;11(1):77-80. Published online January 29, 2024; DOI: https://doi.org/10.6087/kcse.324

2,082 View
102 Download

PDF

Meeting report on the Ninth International Congress on Peer Review and Scientific Publication: Young Yoo; Sci Ed. 2023;10(1):109-112. Published online February 16, 2023; DOI: https://doi.org/10.6087/kcse.303

1,846 View
211 Download

PDF

Original Article

Historical diagnostic and therapeutic changes of ischemic stroke based on the highly cited articles: Yerim Kim, Dae Young Yoon, Jee-Eun Kim, Ju-Hun Lee, Hong-Ki Song, Jong Seok Bae; Sci Ed. 2020;7(2):156-162. Published online August 20, 2020; DOI: https://doi.org/10.6087/kcse.211

6,199 View
94 Download
2 Web of Science
2 Crossref

Abstract PDF Supplementary Material

Purpose
Stroke is a global economic burden of health, which means that it is critical to evaluate changes of optimal diagnoses and treatments. The aim of the study reported herein was to identify the most-cited articles in the field of ischemic stroke and assess their characteristics. Methods: We searched all included articles that had been cited more than 100 times using the “Cited Reference Search” in February 2016 from Web of Science Core Collection. Among a total of 2,651 articles, we excluded articles on basic science and which involved only hemorrhagic strokes and identified the top-100 cited articles on ischemic stroke. Results: The number of citations for the articles analyzed in this study ranged from 5,182 to 580. Most of the articles were published in The Lancet (25%) and Stroke (23%), and originated from the United States of America (n=44). Most of them were original articles (65%) and dealt with stroke risk factors (32%) and stroke management (30%). A novel study of hyperacute treatment involving recombinant-tissue plasminogen activator was described in the top-ranked article. Conclusion: Reviews on highly cited articles can help physicians identify trends in the diagnosis and treatment of ischemic stroke in the past. This bibliometric analysis can provide a unique perspective on historical developments in this field.

Citations

Citations to this article as recorded by

Promotion to Top-Tier Journal and Development Strategy of the Annals of Laboratory Medicine for Strengthening its Leadership in the Medical Laboratory Technology Category: A Bibliometric Study
Sun Huh
Annals of Laboratory Medicine.2022; 42(3): 321. CrossRef
Document Network and Conceptual and Social Structures of Clinical Endoscopy from 2015 to July 2021 Based on the Web of Science Core Collection: A Bibliometric Study
Sun Huh
Clinical Endoscopy.2021; 54(5): 641. CrossRef

Review

Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review: Seong Ho Park, Young-Hak Kim, Jun Young Lee, Soyoung Yoo, Chong Jai Kim; Sci Ed. 2019;6(2):91-98. Published online June 19, 2019; DOI: https://doi.org/10.6087/kcse.164

16,578 View
460 Download
18 Web of Science
19 Crossref

Abstract PDF

This review article aims to highlight several areas in research studies on artificial intelligence (AI) in medicine that currently require additional transparency and explain why additional transparency is needed. Transparency regarding training data, test data and results, interpretation of study results, and the sharing of algorithms and data are major areas for guaranteeing ethical standards in AI research. For transparency in training data, clarifying the biases and errors in training data and the AI algorithms based on these training data prior to their implementation is critical. Furthermore, biases about institutions and socioeconomic groups should be considered. For transparency in test data and test results, authors should state if the test data were collected externally or internally and prospectively or retrospectively at first. It is necessary to distinguish whether datasets were convenience samples consisting of some positive and some negative cases or clinical cohorts. When datasets from multiple institutions were used, authors should report results from each individual institution. Full publication of the results of AI research is also important. For transparency in interpreting study results, authors should interpret the results explicitly and avoid over-interpretation. For transparency by sharing algorithms and data, sharing is required for replication and reproducibility of the research by other researchers. All of the above mentioned high standards regarding transparency of AI research in healthcare should be considered to facilitate the ethical conduct of AI research.

Citations

Citations to this article as recorded by

Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare: State-of-the-Art and Future Prospects
Talha Iqbal, Mehedi Masud, Bilal Amin, Conor Feely, Mary Faherty, Tim Jones, Michelle Tierney, Atif Shahzad, Patricia Vazquez
Health Sciences Review.2024; : 100150. CrossRef
The Knowledge of Students at Bursa Faculty of Medicine towards Artificial Intelligence: A Survey Study
Deniz GÜVEN, Elif Güler KAZANCI, Ayşe ÖREN, Livanur SEVER, Pelin ÜNLÜ
Journal of Bursa Faculty of Medicine.2024; 2(1): 20. CrossRef
New institutional theory and AI: toward rethinking of artificial intelligence in organizations
Ihor Rudko, Aysan Bashirpour Bonab, Maria Fedele, Anna Vittoria Formisano
Journal of Management History.2024;[Epub] CrossRef
Benefits and Challenges of Using AI for Peer Review: A Study on Researchers’ Perceptions
Louie Giray
The Serials Librarian.2024; : 1. CrossRef
The role of explainability and transparency in fostering trust in AI healthcare systems: a systematic literature review, open issues and potential solutions
Christopher Ifeanyi Eke, Liyana Shuib
Neural Computing and Applications.2024;[Epub] CrossRef
Artificial intelligence technology in MR neuroimaging. А radiologist’s perspective
G. E. Trufanov, A. Yu. Efimtsev
Russian Journal for Personalized Medicine.2023; 3(1): 6. CrossRef
The minefield of indeterminate thyroid nodules: could artificial intelligence be a suitable diagnostic tool?
Vincenzo Fiorentino, Cristina Pizzimenti, Mariausilia Franchina, Marina Gloria Micali, Fernanda Russotto, Ludovica Pepe, Gaetano Basilio Militi, Pietro Tralongo, Francesco Pierconti, Antonio Ieni, Maurizio Martini, Giovanni Tuccari, Esther Diana Rossi, Gu
Diagnostic Histopathology.2023; 29(8): 396. CrossRef
Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review
Anto Čartolovni, Ana Tomičić, Elvira Lazić Mosler
International Journal of Medical Informatics.2022; 161: 104738. CrossRef
Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide
Jose Bernal, Claudia Mazo
Applied Sciences.2022; 12(20): 10228. CrossRef
Artificial intelligence in the water domain: Opportunities for responsible use
Neelke Doorn
Science of The Total Environment.2021; 755: 142561. CrossRef
Artificial intelligence for ultrasonography: unique opportunities and challenges
Seong Ho Park
Ultrasonography.2021; 40(1): 3. CrossRef
Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence
Seong Ho Park, Jaesoon Choi, Jeong-Sik Byeon
Korean Journal of Radiology.2021; 22(3): 442. CrossRef
Is it alright to use artificial intelligence in digital health? A systematic literature review on ethical considerations
Nicholas RJ Möllmann, Milad Mirbabaie, Stefan Stieglitz
Health Informatics Journal.2021;[Epub] CrossRef
Presenting machine learning model information to clinical end users with model facts labels
Mark P. Sendak, Michael Gao, Nathan Brajer, Suresh Balu
npj Digital Medicine.2020;[Epub] CrossRef
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
Zeeshan Ahmed, Khalid Mohamed, Saman Zeeshan, XinQi Dong
Database.2020;[Epub] CrossRef
The ethics of machine learning in medical sciences: Where do we stand today?
Treena Basu, Sebastian Engel-Wolf, Olaf Menzer
Indian Journal of Dermatology.2020; 65(5): 358. CrossRef
Key principles of clinical validation, device approval, and insurance coverage decisions of artificial intelligence
Seong Ho Park, Jaesoon Choi, Jeong-Sik Byeon
Journal of the Korean Medical Association.2020; 63(11): 696. CrossRef
Reflections as 2020 comes to an end: the editing and educational environment during the COVID-19 pandemic, the power of Scopus and Web of Science in scholarly publishing, journal statistics, and appreciation to reviewers and volunteers
Sun Huh
Journal of Educational Evaluation for Health Professions.2020; 17: 44. CrossRef
What should medical students know about artificial intelligence in medicine?
Seong Ho Park, Kyung-Hyun Do, Sungwon Kim, Joo Hyun Park, Young-Suk Lim
Journal of Educational Evaluation for Health Professions.2019; 16: 18. CrossRef

First
Prev
Page of 1
Next
Last