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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
  • 1,009 View
  • 62 Download
AbstractAbstract 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.
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
  • 5,133 View
  • 118 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract 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.

Citations

Citations to this article as recorded by  
  • 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
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
  • 8,595 View
  • 177 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract 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

Citations to this article as recorded by  
  • 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
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
  • 7,448 View
  • 205 Download
  • 2 Web of Science
  • 5 Crossref
AbstractAbstract PDFSupplementary 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

Citations to this article as recorded by  
  • Sense and sensibility of article submission platforms are needed regarding verification of AI use: a stakeholders’ perspective
    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
  • 3,890 View
  • 88 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract 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
  • 10,748 View
  • 201 Download
  • 5 Web of Science
  • 2 Crossref
AbstractAbstract 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

Citations to this article as recorded by  
  • Computational Modeling and Digital Twin Technologies in Medical Device Development
    Champa Tudu, Sarita Sharma, Dheeraj Kumar
    Biomedical Materials & Devices.2025;[Epub]     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
  • 13,699 View
  • 195 Download
  • 11 Web of Science
  • 13 Crossref
AbstractAbstract 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.

Citations

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  • Neural Efficiency and Attentional Instability in Gaming Disorder: A Task-Based Occipital EEG and Machine Learning Study
    Riaz Muhammad, Ezekiel Edward Nettey-Oppong, Muhammad Usman, Saeed Ahmed Khan Abro, Toufique Ahmed Soomro, Ahmed Ali
    Bioengineering.2026; 13(2): 152.     CrossRef
  • Development and validation of a multimodal data collection system for adolescent mental health management
    Siyeon Ko, Kyoungsu Oh, Uhyeong Won, Jung-A Oh, Nak-Jung Kwon, Hyun-sook Park, Young-A Ji, Sungjin Kim, Yonghwan Moon, Nayoung Park, Dohyoung Kim, Euijun Yang, Kyungmin Na, Yeonju Kim, Youngho Lee, Hyekyung Woo
    DIGITAL HEALTH.2026;[Epub]     CrossRef
  • Multi-omics biomarkers in psychiatric disorders diagnosis and stratification
    Seyyed Hossein Khatami, Sanam Anoosheh, Marzieh Khodaparast, Amir Maghsoudloonejad, Ehsan Dadgostar, Amir Asadi, Mahya Kaveh, Malihe Mehdinejad Haghighi
    Clinica Chimica Acta.2026; 585: 120887.     CrossRef
  • Lymphocyte subpopulations and EEG asymmetry
    Matisse Ducharme, Reza Zomorrodi, George Nader, Corinne Fischer, Philip Gerretsen, Ariel Graff, Daniel Blumberger, Vincenzo De Luca
    Journal of Neural Transmission.2026;[Epub]     CrossRef
  • Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals
    Gulay Tasci, Prabal Datta Barua, Dahiru Tanko, Tugce Keles, Suat Tas, Ilknur Sercek, Suheda Kaya, Kubra Yildirim, Yunus Talu, Burak Tasci, Filiz Ozsoy, Nida Gonen, Irem Tasci, Sengul Dogan, Turker Tuncer
    Diagnostics.2025; 15(2): 154.     CrossRef
  • Innovative Therapeutic Approaches in Severe Adolescent Depression: Neuroimaging and Pharmacological Insights
    Andrei-Gabriel Zanfir, Simona-Corina Trifu
    Balneo and PRM Research Journal.2025; 16(Vol 16 No.): 765.     CrossRef
  • Epileptic Seizure Detection Using Machine Learning: A Systematic Review and Meta-Analysis
    Lin Bai, Gerhard Litscher, Xiaoning Li
    Brain Sciences.2025; 15(6): 634.     CrossRef
  • A Systematic Review of Mental Health Monitoring and Intervention Using Unsupervised Deep Learning on EEG Data
    Akhila Reddy Yadulla, Guna Sekhar Sajja, Santosh Reddy Addula, Mohan Harish Maturi, Geeta Sandeep Nadella, Elyson De La Cruz, Karthik Meduri, Hari Gonaygunta
    Psychology International.2025; 7(3): 61.     CrossRef
  • A recent advances on autism spectrum disorders in diagnosing based on machine learning and deep learning
    Hajir Ammar Hatim, Zaid Abdi Alkareem Alyasseri, Norziana Jamil
    Artificial Intelligence Review.2025;[Epub]     CrossRef
  • High alpha oscillations in portable prefrontal EEG indicate gender-sensitive biomarkers for emotional disorders
    Shu Tang, Chuanliang Han, Xuebing Li
    Scientific Reports.2025;[Epub]     CrossRef
  • Interhemispheric EEG coherence as a candidate biomarker in gambling disorder: evidence of frontal hyperconnectivity and posterior disconnectivity
    Eda Yılmazer, Metin Çinaroğlu, Selami Varol Ülker, Sultan Tarlacı
    Frontiers in Neuroscience.2025;[Epub]     CrossRef
  • HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage
    Jian Shi, Danyang Chen, Xingwei Zhao, Zhixian Zhao, Shengjie Li, Yeguang Xu, Tao Ding, Zheng Zhu, Peng Zhang, Qing Ye, Yingxin Tang, Ping Zhang, Bo Tao, Zhouping Tang
    Scientific Data.2025;[Epub]     CrossRef
  • Predicting Major Depressive Disorder Using Neural Networks from Spectral Measures of EEG Data
    Igor Kozulin, Ekaterina Merkulova, Vasiliy Savostyanov, Haonan Shi, Xinyi Wang, Andrey Bocharov, Alexander Savostyanov
    Bioengineering.2025; 12(11): 1251.     CrossRef
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
  • 9,254 View
  • 208 Download
  • 17 Web of Science
  • 21 Crossref
AbstractAbstract 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.

Citations

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  • Development of a deep learning classification model using a codeless platform for orthodontic extraction decision-making: Impact of image type on model performance
    KyungMin Clara Lee
    Journal of Dentistry.2026; 166: 106296.     CrossRef
  • Advancements in artificial intelligence algorithms for dental implant identification: A systematic review with meta-analysis
    Ahmed Yaseen Alqutaibi, Radhwan S. Algabri, Dina Elawady, Wafaa Ibrahim Ibrahim
    The Journal of Prosthetic Dentistry.2025; 134(4): 1089.     CrossRef
  • Artificial intelligence for dental implant classification and peri-implant pathology identification in 2D radiographs: A systematic review
    M Bonfanti-Gris, E Ruales, MP Salido, F Martinez-Rus, M Özcan, G Pradies
    Journal of Dentistry.2025; 153: 105533.     CrossRef
  • Artificial Intelligence in Detecting and Segmenting Vertical Misfit of Prosthesis in Radiographic Images of Dental Implants: A Cross‐Sectional Analysis
    Paniz Fasih, Amir Yari, Lotfollah Kamali Hakim, Nader Nasim Kashe
    Clinical Oral Implants Research.2025; 36(5): 578.     CrossRef
  • Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions
    Sohaib Shujaat
    Diagnostics.2025; 15(3): 273.     CrossRef
  • Advanced deep learning techniques for recognition of dental implants
    Veena Benakatti, Ramesh P. Nayakar, Mallikarjun Anandhalli, Rohit sukhasare
    Journal of Oral Biology and Craniofacial Research.2025; 15(2): 215.     CrossRef
  • Optimized classification of dental implants using convolutional neural networks and pre-trained models with preprocessed data
    Reza Ahmadi Lashaki, Zahra Raeisi, Nasim Razavi, Mehdi Goodarzi, Hossein Najafzadeh
    BMC Oral Health.2025;[Epub]     CrossRef
  • 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
  • Assessment of the Diagnostic Accuracy of Artificial Intelligence Software in Identifying Common Periodontal and Restorative Dental Conditions (Marginal Bone Loss, Periapical Lesion, Crown, Restoration, Dental Caries) in Intraoral Periapical Radiographs
    Wael I. Ibraheem, Saurabh Jain, Mohammed Naji Ayoub, Mohammed Ahmed Namazi, Amjad Ismail Alfaqih, Aparna Aggarwal, Abdullah A. Meshni, Ammar Almarghlani, Abdulkareem Abdullah Alhumaidan
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    G. Vázquez-Sebrango, E. Anitua, I. Macía, I. Arganda-Carreras
    International Journal of Oral and Maxillofacial Surgery.2025; 54(11): 1098.     CrossRef
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    Veena Benakatti, Ramesh P. Nayakar, Mallikarjun Anandhalli, Rohit C. Sukhasare
    Imaging Science in Dentistry.2025; 55(4): 351.     CrossRef
  • Automated assessment of peri-implant disease severity by deep learning and image processing in periapical radiographs
    Yi-Cheng Mao, Chiung-An Chen, Yuan-Jin Lin, Yu-Jen Chang, Sung-Tsun Wei, Shih-Lun Chen, Tsung-Yi Chen, Kuo-Chen Li, Wei-Chen Tu, Patricia Angela R. Abu
    Journal of Dental Sciences.2025;[Epub]     CrossRef
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    Hatice Tekis, Taha Zirek, Melek Tassoker
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    Mohammed A. H. Lubbad, Ikbal Leblebicioglu Kurtulus, Dervis Karaboga, Kerem Kilic, Alper Basturk, Bahriye Akay, Ozkan Ufuk Nalbantoglu, Ozden Melis Durmaz Yilmaz, Mustafa Ayata, Serkan Yilmaz, Ishak Pacal
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    Cristiana Adina Șalgău, Anca Morar, Andrei Daniel Zgarta, Diana-Larisa Ancuța, Alexandros Rădulescu, Ioan Liviu Mitrea, Andrei Ovidiu Tănase
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    Olivia Bobeică, Denis Iorga
    Journal of Dentistry.2024; 151: 105385.     CrossRef
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    Mohamed A.E. Elsayed, Radwa A. El-dessouky, Mahmoud A.-A. Shakal
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  • Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry
    Amal Alfaraj, Toshiki Nagai, Hawra AlQallaf, Wei-Shao Lin
    Dentistry Journal.2024; 13(1): 13.     CrossRef

JYMS : Journal of Yeungnam Medical Science
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