Science Editing

Meeting Reports

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

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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

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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

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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
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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

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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

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Health Sciences Review.2024; : 100150. CrossRef
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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
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