Kosin Medical Journal

Review articles

The ethics of using artificial intelligence in medical research: Shinae Yu, Sang-Shin Lee, Hyunyong Hwang; Kosin Med J. 2024;39(4):229-237. Published online December 6, 2024; DOI: https://doi.org/10.7180/kmj.24.140

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The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

Citations

Citations to this article as recorded by

Integrating Artificial Intelligence in Orthopedic Care: Advancements in Bone Care and Future Directions
Rahul Kumar, Kyle Sporn, Joshua Ong, Ethan Waisberg, Phani Paladugu, Swapna Vaja, Tamer Hage, Tejas C. Sekhar, Amar S. Vadhera, Alex Ngo, Nasif Zaman, Alireza Tavakkoli, Mouayad Masalkhi
Bioengineering.2025; 12(5): 513. CrossRef
Current Bioinformatics Tools in Precision Oncology
Tesfaye Wolde, Vipul Bhardwaj, Vijay Pandey
MedComm.2025;[Epub] CrossRef
Case Report: Intranasal esketamine combined with a form of generative artificial intelligence in the management of treatment-resistant depression
Alexandre Fraichot, Sophie Favre, Hélène Richard-Lepouriel
Frontiers in Psychiatry.2025;[Epub] CrossRef
Future Designs of Clinical Trials in Nephrology: Integrating Methodological Innovation and Computational Power
Camillo Tancredi Strizzi, Francesco Pesce
Sensors.2025; 25(16): 4909. CrossRef
The revolutionary impact of artificial intelligence in orthopedics: comprehensive review of current benefits and challenges
Salar Baghbani, Yoosef Mehrabi, Mohammad Movahedinia, Erfan Babaeinejad, Mohammadamin Joshaghanian, Shayan Amiri, Mostafa Shahrezaee
Journal of Robotic Surgery.2025;[Epub] CrossRef
Human-in-the-Loop Performance of LLM-Assisted Arterial Blood Gas Interpretation: A Single-Center Retrospective Study
Sergio Ayala-De la Cruz, Paola Elizabeth Arenas-Hernández, María Fernanda Fernández-Herrera, Rebeca Alejandrina Quiñones-Díaz, Jorge Martín Llaca-Díaz, Erik Alejandro Díaz-Chuc, Diana Guadalupe Robles-Espino, Erik Alejandro San Miguel-Garay
Journal of Clinical Medicine.2025; 14(18): 6676. CrossRef
Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review
Yuhang Lin, Zhiheng Luo, Zicheng Ye, Nuoxi Zhong, Lijian Zhao, Long Zhang, Xiaolan Li, Zetao Chen, Yijia Chen
JMIR Medical Education.2025; 11: e71125. CrossRef
Ética, empatia e IA: como equilibrar decisões automatizadas e julgamento clínico humanizado
Iranildo Lopes de Oliveira, Rivana Ferreira de Souza, João Victor de Amorim Batista, Iara Costa Silvano, Ana Caroline Rocha de Melo Leite, Rodolfo de Melo Nunes
Cuadernos de Educación y Desarrollo.2025; 17(10): e9794. CrossRef
Machine learning in lupus nephritis: bridging prediction models and clinical decision-making towards personalized nephrology
Diego Fernando Garcia-Bañol, Adrianny Mahelis Arias-Choles, Silvia Aldana-Peréz, Gustavo J. Aroca-Martínez, Carlos Guido Musso, Roberto Navarro-Quiroz, Alex Dominguez-Vargas, Henry J. Gonzalez-Torres
Frontiers in Medicine.2025;[Epub] CrossRef
Ethical Integration of Artificial Intelligence in Nursing Research: An Evidence-based Practice Project from Saudi Arabia
Jennifer de Beer, Khulud Bababkr Mohammed, Joynalyn Barrios, Salma Elnajjar, Meead Fawaz Aldabahy, Vimela Moodley, Asma Almuntashiri, Maab Basha, Ashwag Othman Eissa, Wejdan Omar Barayan, Shonise Young
Journal of Nursing Science and Professional Practice.2025; 2(4): 183. CrossRef

Considerations for experimental animal ethics in the research planning and evaluation process: Jaewon Shim, Jeongtae Kim; Kosin Med J. 2022;37(4):271-277. Published online December 22, 2022; DOI: https://doi.org/10.7180/kmj.22.139

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6 Citations

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Research using experimental animals has substantially contributed to advances in science and medicine. Animal experiments are nearly essential for biomedical research and development efforts. Because many animals are sacrificed, researchers should consider the welfare of experimental animals and related ethical issues, along with the successful results of their experiments. This review introduces the criteria that should be considered in terms of experimental animal ethics, based on the principles of the 3 R’s: replacement, representing careful consideration of the need for animal experiments; reduction, representing the use of the minimal number of animals to obtain meaningful experimental results; and refinement, representing continuous effects to find alternative methods to reduce pain and distress in experimental animals. Based on these principles, the following points should be considered when planning experiments: the necessity of animal experiments; alternatives to animal experiments; the relevance of the species and numbers of experimental animals; appropriate assessment and management of pain; the proper usage of sedatives, painkillers, and anesthesia; and valid timing for humane endpoints and euthanasia. These criteria are beneficial for both experimental animals and researchers because careful handling to ensure experimental animal welfare guarantees that scientific research will yield convincing, repeatable, and accurate results.

Citations

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Bacillus subtilis 7A Enhances the Nutritional Quality of Velvet Bean (Mucuna pruriens Linn.) Seeds and Potentiates Its Role in the Management of Type II Diabetes Mellitus
Omodele Ibraheem, Pelumi D. Ibikunle, Ruth A. O. Gabriel‐Ajobiewe, Olubukola H. Oyeniran, Grace O. Sodipe, Titilope R. Komolafe, Kayode Komolafe, Oluwasogo T. Oyewole, Emmanuel C. Ogwa, Temitope O. Jeje
Journal of Food Science.2025;[Epub] CrossRef
Functional Analysis of Membrane-Associated Scaffolding Tight Junction (TJ) Proteins in Tumorigenic Characteristics of B16-F10 Mouse Melanoma Cells
Eun-Ji Ko, Do-Ye Kim, Min-Hye Kim, Hyojin An, Jeongtae Kim, Jee-Yeong Jeong, Kyoung Seob Song, Hee-Jae Cha
International Journal of Molecular Sciences.2024; 25(2): 833. CrossRef
General Principles, Designs, and Statistical Analyses in Experimental Animal Studies
Şengül Cangür
Düzce Tıp Fakültesi Dergisi.2024; 26(S1): 14. CrossRef
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Animal Models and Experimental Medicine.2024; 7(5): 673. CrossRef

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