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Meeting Reports
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
  • 1,369 View
  • 204 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
  • 5,277 View
  • 90 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary 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
  • 15,009 View
  • 417 Download
  • 16 Web of Science
  • 17 Crossref
AbstractAbstract 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
  • 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; 27(4): 146045822110523.     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

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