Journal of Yeungnam Medical Science

Review articles

Psychiatry and Mental Health
Artificial intelligence anxiety: a narrative review of psychiatric conceptualization and clinical management: So Hye Jo; J Yeungnam Med Sci. 2026;43:54. Published online August 10, 2026; DOI: https://doi.org/10.12701/jyms.2026.43.54

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Abstract PDF: Artificial intelligence (AI) is gradually permeating every aspect of daily life. AI is now more than a mere tool; it significantly influences individual capabilities, professional values, social status, and our perspectives on the future. Amid these AI-driven changes, the concept of AI anxiety (AIA) has emerged to describe the associated emotional distress; however, its psychiatric significance has not yet been clearly defined. Presently, AIA can be conceptualized not as a distinct psychiatric diagnosis, but as a stress response capable of amplifying pre-existing vulnerabilities, psychiatric symptoms, maladaptive coping mechanisms, and functional impairments. This review categorizes AIA into five interrelated domains: anxiety regarding competence and adaptation, anxiety concerning occupational displacement and role loss, anxiety related to sociotechnical mistrust and loss of control, anxiety regarding identity and the human-machine boundary, and existential or catastrophic anxiety regarding the future. The clinical assessment of patients should evaluate specific AI-related experiences, perceived threats, behavioral responses, functional consequences, comorbid symptoms, and reality-testing abilities. Cases accompanied by excessive reliance on AI, social isolation, or the substitution of human support with AI interactions warrant increased attention. Therapeutic approaches include psychoeducation, cognitive and behavioral strategies, acceptance-based approaches, meaning-centered psychotherapy, AI literacy education, and efforts to restore human connections. Future studies should clarify the definition and scope of AIA and elucidate its course and treatment responsiveness through longitudinal and clinical studies using reliable assessment tools.

Medical Informatics
Are artificial intelligence (AI) agents ready for medicine and biomedical research? A narrative review: Sangzin Ahn; J Yeungnam Med Sci. 2026;43:40. Published online June 17, 2026; DOI: https://doi.org/10.12701/jyms.2026.43.40

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Abstract PDF: Artificial intelligence (AI) agents extend large language models from single-turn text generation to systems that pursue goals through planning, retrieval, tool use, code execution, memory, feedback, and role coordination. In medicine and biomedical research, this shift is creating early systems for clinical calculations, risk prediction, oncology decision support, omics analysis, hypothesis development, laboratory automation, and research writing. However, the evidence remains uneven. Clinical examples are the most defensible when agents use validated calculators, curated clinical tools, or guideline-grounded modules under human oversight. Biomedical discovery systems exhibit broader workflow capabilities; however, many claims still rely on preprints, narrow benchmarks, simulated settings, or domain-specific demonstrations. For clinicians and biomedical researchers, the immediate challenge is not to decide whether agents will replace experts but to understand what tasks can be delegated, what evidence is needed, and what human judgment must be preserved. This narrative review explains what makes an AI system agentic, summarizes its representative clinical and discovery applications, and outlines safeguards for evaluation, reproducibility, and oversight. Biomedical readers should expect AI agents to enter medicine and research first as constrained, auditable workflow infrastructures. These infrastructures may reorganize biomedical work; however, accountability should remain with the clinicians and investigators.

Original article

Medical Education
Mediating effect of technostress on the relationship between artificial intelligence literacy and attitude toward digital technology among health professions students: a structural equation modeling approach: Jin Young Lee, Yul Ha Min, Jun Yim, Kwi Hwa Park, So Jung Yune; J Yeungnam Med Sci. 2026;43:7. Published online December 30, 2025; DOI: https://doi.org/10.12701/jyms.2026.43.7

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Abstract PDF

Background
This study aimed to examine the effect of artificial intelligence (AI) literacy on attitudes toward digital technology and the mediating effect of technostress on this relationship among health professions students.
Methods
An online survey was conducted from May to October 2025 with 1,314 students enrolled in medical schools, nursing schools, dental schools, and graduate schools of dentistry nationwide. Structural equation modeling and bootstrapping analyses were performed.
Results
The analysis revealed that AI literacy significantly reduced technostress and enhanced attitudes toward digital technology. Technostress also had a negative effect on attitudes toward digital technology, and a partial mediating effect was identified in the relationship between AI literacy and attitudes toward digital technology. In other words, higher levels of AI literacy were associated with lower technostress, which, in turn, led to more positive attitudes toward digital technology. Multigroup analysis further showed that the effect of AI literacy on technostress differed across majors, being significant for medical and nursing students, but not for dental students.
Conclusion
This study confirmed that improving AI literacy reduces technology-related stress and promotes positive attitudes toward digital technology. These findings suggest the need for AI and digital technology education designs that consider the psychological factors of learners in medical education. Furthermore, the observed group differences suggest that AI literacy may function differently depending on discipline-specific technological and educational contexts.

Citations

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AI health literacy as a foundation for responsible AI in healthcare: a framework for trustworthy implementation
Olga Tzortzatou Nanopoulou
Frontiers in Digital Health.2026;[Epub] CrossRef
Artificial intelligence anxiety: a narrative review of psychiatric conceptualization and clinical management
So Hye Jo
Journal of Yeungnam Medical Science.2026; 43: 54. CrossRef
Artificial intelligence readiness and its association with artificial intelligence literacy among Chinese medical students: a latent profile analysis
Zongsheng Tang, Shuhong Song, Xinyue Chen, Jiaxu Qin, Yan Li, Jingjing Yu, Chen Yu, Huan Liu, Xiangxiang Tao
Frontiers in Public Health.2026;[Epub] CrossRef

Communications

Medical Informatics
Emerging technologies in the field of medicine presented at the Consumer Electronics Show 2025: Jong-Ryul Yang, Min Cheol Chang; J Yeungnam Med Sci. 2025;42:31. Published online April 1, 2025; DOI: https://doi.org/10.12701/jyms.2025.42.31

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Abstract PDF

The Consumer Electronics Show 2025 highlighted innovative technologies with considerable potential for healthcare, particularly artificial intelligence (AI) and sensor technologies. Notable advances that were showcased included products that leverage AI to personalize health management, such as devices capable of recommending binaural beat stimulation, analyzing speech patterns to detect language impairment, and predicting blood pressure through sleep data analysis. AI applications to enhance sleep quality, reduce snoring, and assess the caloric content of children’s meals were presented. However, the accuracy of these products remains inadequate for clinical use, which limits their applications in community settings. This showcase also featured advances in both contact and noncontact sensor technologies. Contact-type sensors, such as wearable rings and sensors designed to measure vital signs, including pulse rate, blood glucose, and blood pressure, have been developed to mitigate discomfort while maintaining high accuracy. Noncontact sensors employing radar and remote photoplethysmography technologies have further demonstrated promise for vital sign monitoring without physical contact, although maintaining accuracy during movement remains a challenge. AI integration with sensors was further demonstrated by the development of an electronic stethoscope utilizing microelectromechanical systems and deep learning algorithms to facilitate the perception of heart and breath sounds, emulating the functionality of conventional stethoscopes. Furthermore, advances in laser-based blood glucose monitoring and wearable robotic belts designed to assist gait have underscored the progress in devices aimed at enhancing patient care and daily living. These technologies hold considerable potential to profoundly transform healthcare systems, particularly in home and community settings.

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A review of the application of nanotechnology-based, non-contact remote patient monitoring in intelligent nursing for the prevention of major adverse cardiovascular events
Xiaoya Liu, Li Min, Yuandong Tao, Li Han, Lingfei Su, Ling Wu, Xuhong Pan, Ming Zhang, Fangming Guo, Xueqin Ding
Nanomedicine.2026; 21(2): 255. CrossRef
Blockchain-Enabled EEG Neuromarketing Framework for Secure and Intelligent Consumer Electronics
Usama Arshad, Abdallah Tubaishat, Sajid Anwar, Zahid Halim, Yoshinobu Kawahara
IEEE Transactions on Consumer Electronics.2026; 72(3): 6796. CrossRef

Review article

Radiology, Radiotherapy & Diagnostic Imaging
Digital auscultation in clear and present threat of novel respiratory infectious disease: a narrative review: Heeeon Lee, Gun Kim, Jacob Sangwoon Bae; J Yeungnam Med Sci. 2025;42:19. Published online December 30, 2024; DOI: https://doi.org/10.12701/jyms.2025.42.19

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Abstract PDF

The coronavirus disease 2019 pandemic has underscored the limitations of traditional diagnostic methods, particularly in ensuring the safety of healthcare workers and patients during infectious outbreaks. Smartphone-based digital stethoscopes enhanced with artificial intelligence (AI) have emerged as potential tools for addressing these challenges by enabling remote, efficient, and accessible auscultation. Despite advancements, most existing systems depend on additional hardware and external processing, increasing costs and complicating deployment. This review examines the feasibility and limitations of smartphone-based digital stethoscopes powered by AI, focusing on their ability to perform real-time analyses of audible and inaudible sound frequencies. We also explore the regulatory barriers, data storage challenges, and diagnostic accuracy issues that must be addressed to facilitate broader adoption. The implementation of these devices in veterinary medicine is discussed as a practical step toward refining their applications. With targeted improvements and careful consideration of existing limitations, smartphone-based AI stethoscopes could enhance diagnostic capabilities in human and animal healthcare settings.

Citations

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Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation
Hien Thi Thu Nguyen, Vang Le-Quy, Anh Tuan Dinh-Xuan, Linh Nhat Nguyen
Diagnostics.2026; 16(8): 1127. CrossRef
LMS-ViT: a multi-scale vision transformer approach for real-time smartphone-based skin cancer detection
A. Anny Leema, P. Balakrishnan, G. Gopichand, G. Rajarajan
Frontiers in Artificial Intelligence.2025;[Epub] CrossRef
Design Evolution and Feature Enhancement Strategies for Advanced Digital Stethoscopes
Madeeha Akbar, Sayed Shahid Hussain, Shahzad Anwar, Sheeraz Ahmed
International Journal of Innovations in Science and Technology.2025; : 2429. CrossRef

Original article

Medical Informatics
Large language model usage guidelines in Korean medical journals: a survey using human-artificial intelligence collaboration: Sangzin Ahn; J Yeungnam Med Sci. 2025;42:14. Published online December 11, 2024; DOI: https://doi.org/10.12701/jyms.2024.00794

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Abstract PDF Supplementary Material

Background
Large language models (LLMs), the most recent advancements in artificial intelligence (AI), have profoundly affected academic publishing and raised important ethical and practical concerns. This study examined the prevalence and content of AI guidelines in Korean medical journals to assess the current landscape and inform future policy implementation.
Methods
The top 100 Korean medical journals determined by Hirsh index were surveyed. Author guidelines were collected and screened by a human researcher and AI chatbot to identify AI-related content. The key components of LLM policies were extracted and compared across journals. The journal characteristics associated with the adoption of AI guidelines were also analyzed.
Results
Only 18% of the surveyed journals had LLM guidelines, which is much lower than previously reported in international journals. However, the adoption rates increased over time, reaching 57.1% in the first quarter of 2024. High-impact journals were more likely to have AI guidelines. All journals with LLM guidelines required authors to declare LLM tool use and 94.4% prohibited AI authorship. The key policy components included emphasizing human responsibility (72.2%), discouraging AI-generated content (44.4%), and exempting basic AI tools (38.9%).
Conclusion
While the adoption of LLM guidelines among Korean medical journals is lower than the global trend, there has been a clear increase in implementation over time. The key components of these guidelines align with international standards, but greater standardization and collaboration are needed to ensure the responsible and ethical use of LLMs in medical research and writing.

Citations

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How editors perceive the use of generative artificial intelligence in writing academic papers: a narrative review
Sun Huh
Journal of the Korean Medical Association.2026; 69(2): 111. CrossRef
Research hotspots and trends of artificial intelligence ethics in medicine: A bibliometric analysis from 1999 to 2025
Jialu Li, Jun Li, Yunjing Qiu, Shiquan He, Zhenxiang Zhang, Peng Wang, Hui Zhang, Beilei Lin
DIGITAL HEALTH.2026;[Epub] CrossRef
Presence and content of policies on the use of generative artificial intelligence in nursing journals indexed in PubMed: a descriptive study
Geum Hee Jeong, Eun Jeong Ko
Science Editing.2026; 13(2): 103. CrossRef
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Jaime A. Teixeira da Silva, Joshua Wang
AI and Ethics.2025; 5(6): 6127. CrossRef
Large Language Models(LLMs) in Political Science Research: Analysis of Topical Trends and Usage Patterns
Inbok RHEE
The Korean Journal of International Relations.2025; 65(3): 257. CrossRef
Performance of large language models in fluoride-related dental knowledge: a comparative evaluation study of ChatGPT-4, Claude 3.5 Sonnet, Copilot, and Grok 3
Raju Biswas, Atanu Mukhopadhyay, Santanu Mukhopadhyay
Journal of Yeungnam Medical Science.2025; 42: 53. CrossRef
Role of Medical Editors in the Age of Generative Artificial Intelligence
Sun Huh
Healthcare Informatics Research.2025; 31(4): 317. CrossRef
What should researchers do in the era of artificial intelligence?
Min Cheol Chang
Journal of Yeungnam Medical Science.2025; 43: 2. CrossRef

Case report

Medical Informatics
Development of an automated foot contact area measurement program for podoscopes using ChatGPT-4: a case report: Min Cheol Chang; J Yeungnam Med Sci. 2025;42:13. Published online December 3, 2024; DOI: https://doi.org/10.12701/jyms.2024.01326

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Abstract PDF

Accurate measurement of the foot contact area is crucial for diagnosing pes planus (flatfoot) and pes cavus (high arch), which significantly affect pressure distribution across the plantar surface. This study aimed to develop a program using ChatGPT-4 to automate foot contact area measurements using a podoscope, thereby enhancing diagnostic precision. A 53-year-old female volunteer stood on a podoscope to capture images of her feet, which were processed to isolate the foot contours and measure the contact areas. A program developed utilizing ChatCPT-4 was designed to outline the feet, detect contact areas, and calculate their sizes and ratios. The results demonstrated clear visualization of foot contours with automated calculation of the contact area and its ratio to the total foot area. The entire foot area measured 1,091,381.00 pixels, with a contact area of 604,252.50 pixels. The ratio of the ground contact area to the entire foot area was calculated as 55.37%. This method, which employs advanced image-processing techniques powered by ChatGPT-4, demonstrates the potential for integrating artificial intelligence into clinical applications. This approach could improve diagnostic precision and patient outcomes through personalized treatment strategies.

Citations

Citations to this article as recorded by

Emerging technologies in the field of medicine presented at the Consumer Electronics Show 2025
Jong-Ryul Yang, Min Cheol Chang
Journal of Yeungnam Medical Science.2025; 42: 31. CrossRef
What should researchers do in the era of artificial intelligence?
Min Cheol Chang
Journal of Yeungnam Medical Science.2025; 43: 2. CrossRef

Review articles

Emergency and Critical Care Medicine
Advances and utility of digital twins in critical care and acute care medicine: a narrative review: Gabriele A. Halpern, Marko Nemet, Diksha M. Gowda, Oguz Kilickaya, Amos Lal; J Yeungnam Med Sci. 2025;42:9. Published online November 25, 2024; DOI: https://doi.org/10.12701/jyms.2024.01053

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Abstract PDF

Artificial intelligence (AI) has shown promise for revolutionizing healthcare. This narrative review focuses on the evolving discussion of the utility of AI and clinical informatics in critical care and acute care medicine, specifically focusing on digital twin (DT) technology. The improved computational power and iterative validation of these intelligent tools have enhanced medical education, in silico research, and clinical decision support in critical care settings. Integrating DTs into critical care opens vast opportunities, but simultaneously poses complex challenges, from data safety and privacy concerns to potentially increasing healthcare disparities. In medicine, DTs can significantly improve the efficiency of critical care systems. Stakeholder investment is essential for successful implementation and integration of these technologies.

Citations

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Computational Modeling and Digital Twin Technologies in Medical Device Development
Champa Tudu, Sarita Sharma, Dheeraj Kumar
Biomedical Materials & Devices.2026; 4(3): 3114. CrossRef
Rheumatic Digital Twin: Proposed Machine Learning–Based Multimodal Framework to Inform Clinical Decision-Making
Daniyal Selani, Rachel Knevel, Marcel Reinders, Erik B van den Akker
Journal of Medical Internet Research.2026; 28: e86763. CrossRef
AI-enhanced multi-method simulation for intensive care unit digital twinning
Xiang Zhong, Siddharth Vipankumar Abrol Neena, Grace Yao Hou, Amos Lal, Ognjen Gajic, Yue Dong
IISE Transactions on Healthcare Systems Engineering.2026; : 1. CrossRef
Digital twins for cardiopulmonary medicine: the case for pulmonary arterial hypertension
Steven A Niederer, Maximilian Balmus, Christopher Burr, Timothy J A Chico, Richard H Clayton, Wenjia Bai, Camila Rangel-Smith, David J Wagg, Richard D Wilkinson, Sheng-Ya Wang, Alexander M K Rothman, Martin R Wilkins
American Journal of Respiratory and Critical Care Medicine.2026; 212(6): 1203. CrossRef
Digital Twin Awareness, Attitude, and Practice among Indian Critical Care Physicians: A Cross-sectional Study
Ram Singh, Prakash Gondode
Indian Journal of Critical Care Medicine.2026; 30(S1): S92. CrossRef
Privacy-Preserving Clinical Decision Support for Emergency Triage Using LLMs: System Architecture and Real-World Evaluation
Alper Karamanlıoğlu, Berkan Demirel, Onur Tural, Osman Tufan Doğan, Ferda Nur Alpaslan
Applied Sciences.2025; 15(15): 8412. CrossRef

Psychiatry and Mental Health
Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review: Seokho Yun; J Yeungnam Med Sci. 2024;41(4):261-268. Published online September 9, 2024; DOI: https://doi.org/10.12701/jyms.2024.00668

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Abstract PDF

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

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Riaz Muhammad, Ezekiel Edward Nettey-Oppong, Muhammad Usman, Saeed Ahmed Khan Abro, Toufique Ahmed Soomro, Ahmed Ali
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DIGITAL HEALTH.2026;[Epub] CrossRef
Multi-omics biomarkers in psychiatric disorders diagnosis and stratification
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Original article

Dentistry
Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study: Hyun Jun Kong; J Yeungnam Med Sci. 2023;40(Suppl):S29-S36. Published online July 26, 2023; DOI: https://doi.org/10.12701/jyms.2023.00465

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Abstract PDF

Background
This study aimed to evaluate the accuracy and clinical usability of implant system classification using automated machine learning on a Google Cloud platform.
Methods
Four dental implant systems were selected: Osstem TSIII, Osstem USII, Biomet 3i Os-seotite External, and Dentsply Sirona Xive. A total of 4,800 periapical radiographs (1,200 for each implant system) were collected and labeled based on electronic medical records. Regions of interest were manually cropped to 400×800 pixels, and all images were uploaded to Google Cloud storage. Approximately 80% of the images were used for training, 10% for validation, and 10% for testing. Google automated machine learning (AutoML) Vision automatically executed a neural architecture search technology to apply an appropriate algorithm to the uploaded data. A single-label image classification model was trained using AutoML. The performance of the mod-el was evaluated in terms of accuracy, precision, recall, specificity, and F1 score.
Results
The accuracy, precision, recall, specificity, and F1 score of the AutoML Vision model were 0.981, 0.963, 0.961, 0.985, and 0.962, respectively. Osstem TSIII had an accuracy of 100%. Osstem USII and 3i Osseotite External were most often confused in the confusion matrix.
Conclusion
Deep learning-based AutoML on a cloud platform showed high accuracy in the classification of dental implant systems as a fine-tuned convolutional neural network. Higher-quality images from various implant systems will be required to improve the performance and clinical usability of the model.

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