Endocrinology and Metabolism

Review Article

Miscellaneous
How Can Clinicians Leverage Vibe Coding for Machine Learning and Deep Learning Research?: Yoonhwan Lee, Sun Huh; Endocrinol Metab. 2025;40(5):659-667. Published online October 29, 2025; DOI: https://doi.org/10.3803/EnM.2025.2675

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Research applying machine learning and deep learning has become increasingly common in medicine. However, for clinicians lacking Python programming skills, conducting such research has often been an intractable task—even when ample data were available. The emergence of ‘vibe coding’ in 2025 has substantially lowered this barrier to entry. This review defines vibe coding, provides a taxonomy of its available tools, and illustrates its practical application through several use cases. Vibe coding is a goal-oriented process in which the user focuses on the desired outcome, issuing natural language directives for environment setup, functionality specification, and output format. The generative artificial intelligence (AI) then produces and refines the underlying code through an interactive feedback loop. Tools such as generative AI platforms (e.g., ChatGPT, Gemini, Claude), graphical user interface-based agents (e.g., Memex, Replit), AI-augmented editors (e.g., Cursor, Visual Studio Code), and command-line interface (CLI) agents (e.g., Gemini CLI, Codex CLI, Claude Code) are available. Demonstrative case studies using publicly accessible datasets illustrate how clinicians can generate and refine Python scripts for classification tasks with minimal coding expertise. Researchers are encouraged to select an accessible tool and gain hands-on experience with real-world data. The adoption of these tools by clinicians, residents, and medical students may promote broader engagement with machine learning and accelerate medical research.

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Role of Medical Editors in the Age of Generative Artificial Intelligence
Sun Huh
Healthcare Informatics Research.2025; 31(4): 317. CrossRef

Original Articles

Adrenal gland
Deep Learning-Based Adrenal Gland Volumetry for the Prediction of Diabetes: Eu Jeong Ku, Soon Ho Yoon, Seung Shin Park, Ji Won Yoon, Jung Hee Kim; Endocrinol Metab. 2025;40(6):991-1001. Published online June 18, 2025; DOI: https://doi.org/10.3803/EnM.2025.2336

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Abstract PDF Supplementary Material PubReader ePub: Background
The long-term association between adrenal gland volume (AGV) and type 2 diabetes (T2D) remains unclear. We aimed to determine the association between deep learning-based AGV and current glycemic status and incident T2D.
Methods
In this observational study, adults who underwent abdominopelvic computed tomography (CT) for health checkups (2011–2012), but had no adrenal nodules, were included. AGV was measured from CT images using a three-dimensional nnU-Net deep learning algorithm. We assessed the association between AGV and T2D using a cross-sectional and longitudinal design.
Results
We used 500 CT scans (median age, 52.3 years; 253 men) for model development and a Multi-Atlas Labeling Beyond the Cranial Vault dataset for external testing. A clinical cohort included a total of 9708 adults (median age, 52.0 years; 5,769 men). The deep learning model demonstrated a dice coefficient of 0.71±0.11 for adrenal segmentation and a mean volume difference of 0.6± 0.9 mL in the external dataset. Participants with T2D at baseline had a larger AGV than those without (7.3 cm³ vs. 6.7 cm³ and 6.3 cm³ vs. 5.5 cm³ for men and women, respectively, all P<0.05). The optimal AGV cutoff values for predicting T2D were 7.2 cm³ in men and 5.5 cm³ in women. Over a median 7.0-year follow-up, T2D developed in 938 participants. Cumulative T2D risk was accentuated with high AGV compared with low AGV (adjusted hazard ratio, 1.27; 95% confidence interval, 1.11 to 1.46).
Conclusion
AGV, measured using deep learning algorithms, is associated with current glycemic status and can significantly predict the development of T2D.

Thyroid
Deep Learning Technology for Classification of Thyroid Nodules Using Multi-View Ultrasound Images: Potential Benefits and Challenges in Clinical Application: Jinyoung Kim, Min-Hee Kim, Dong-Jun Lim, Hankyeol Lee, Jae Jun Lee, Hyuk-Sang Kwon, Mee Kyoung Kim, Ki-Ho Song, Tae-Jung Kim, So Lyung Jung, Yong Oh Lee, Ki-Hyun Baek; Endocrinol Metab. 2025;40(2):216-224. Published online January 13, 2025; DOI: https://doi.org/10.3803/EnM.2024.2058

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Background
This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules.
Methods
This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images.
Results
Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity.
Conclusion
CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Citations

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Deep Learning for Ultrasound Classification to Identify Noninvasive Follicular Thyroid Neoplasms with Papillary–Like Nuclear Features
I-Hung Chien, Yi-Chiung Hsu, Shih-Ping Cheng
Journal of Imaging Informatics in Medicine.2026;[Epub] CrossRef
Knowledge-Prompted Trustworthy Disentangled Learning for Thyroid Ultrasound Segmentation With Limited Annotations
Wenxu Wang, Weizhen Wang, Qianjin Feng, Yu Zhang, Zhenyuan Ning
IEEE Transactions on Image Processing.2026; 35: 983. CrossRef
Integrating Robotic Bilateral Axillo-Breast Approach Thyroidectomy with Molecular Diagnostics and Artificial Intelligence in Thyroid Cancer Care
Qiang Deng, Xiaoping Men, Duo Jin, Yuzhuo Bai
Biomolecules & Therapeutics.2026; 34(1): 45. CrossRef
Deep Learning for the Diagnosis and Treatment of Thyroid Cancer: A Review
Rili Gao, Shangqing Mai, Song Wang, Wuqiang Hu, Zhangqi Chang, Guozhi Wu, Haixia Guan
Endocrine Practice.2025; 31(12): 1608. CrossRef
Artificial Intelligence for Thyroid Ultrasound: Clinical Performance, Pitfalls, and Practice Integration
Junseok Kang, Jihyun Ahn, Jeong Hun Hah
Clinical Ultrasound.2025; 10(2): 59. CrossRef
Molecular intelligence and immune reconnaissance in thyroid cancer: a new paradigm for diagnosis, risk stratification, and therapeutic precision
Marcio J. Concepción-Zavaleta, Jenyfer M. Fuentes-Mendoza, Alfredo Cruz-Quintá, Argelia V. Cadena-Guerrero, Ximena Barrón, Luis Concepción-Urteaga, Cristian D. Armas, José Paz-Ibarra, Juan Eduardo Quiroz-Aldave
Expert Review of Anticancer Therapy.2025; : 1. CrossRef

Thyroid
Utilizing Immunoglobulin G4 Immunohistochemistry for Risk Stratification in Patients with Papillary Thyroid Carcinoma Associated with Hashimoto Thyroiditis: Faridul Haq, Gyeongsin Park, Sora Jeon, Mitsuyoshi Hirokawa, Chan Kwon Jung; Endocrinol Metab. 2024;39(3):468-478. Published online May 20, 2024; DOI: https://doi.org/10.3803/EnM.2024.1923

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Background
Hashimoto thyroiditis (HT) is suspected to correlate with papillary thyroid carcinoma (PTC) development. While some HT cases exhibit histologic features of immunoglobulin G4 (IgG4)-related disease, the relationship of HT with PTC progression remains unestablished.
Methods
This cross-sectional study included 426 adult patients with PTC (≥1 cm) undergoing thyroidectomy at an academic thyroid center. HT was identified based on its typical histologic features. IgG4 and IgG immunohistochemistry were performed. Wholeslide images of immunostained slides were digitalized. Positive plasma cells per 2 mm² were counted using QuPath and a pre-trained deep learning model. The primary outcome was tumor structural recurrence post-surgery.
Results
Among the 426 PTC patients, 79 were diagnosed with HT. With a 40% IgG4 positive/IgG plasma cell ratio as the threshold for diagnosing IgG4-related disease, a cutoff value of >150 IgG4 positive plasma cells per 2 mm² was established. According to this criterion, 53% (43/79) of HT patients were classified as IgG4-related. The IgG4-related HT subgroup presented a more advanced cancer stage than the IgG4-non-related HT group (P=0.038). The median observation period was 109 months (range, 6 to 142). Initial assessment revealed 43 recurrence cases. Recurrence-free survival periods showed significant (P=0.023) differences, with patients with IgG4 non-related HT showing the longest period, followed by patients without HT and those with IgG4-related HT.
Conclusion
This study effectively stratified recurrence risk in PTC patients based on HT status and IgG4-related subtypes. These findings may contribute to better-informed treatment decisions and patient care strategies.

Citations

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Advanced pathological subtype classification of thyroid cancer using efficientNetB0
Hongpeng Guo, Junjie Zhang, You Li, Xinghe Pan, Chenglin Sun
Diagnostic Pathology.2025;[Epub] CrossRef

Mineral, Bone & Muscle
End-to-End Semi-Supervised Opportunistic Osteoporosis Screening Using Computed Tomography: Jieun Oh, Boah Kim, Gyutaek Oh, Yul Hwangbo, Jong Chul Ye; Endocrinol Metab. 2024;39(3):500-510. Published online May 9, 2024; DOI: https://doi.org/10.3803/EnM.2023.1860

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Background
Osteoporosis is the most common metabolic bone disease and can cause fragility fractures. Despite this, screening utilization rates for osteoporosis remain low among populations at risk. Automated bone mineral density (BMD) estimation using computed tomography (CT) can help bridge this gap and serve as an alternative screening method to dual-energy X-ray absorptiometry (DXA).
Methods
The feasibility of an opportunistic and population agnostic screening method for osteoporosis using abdominal CT scans without bone densitometry phantom-based calibration was investigated in this retrospective study. A total of 268 abdominal CT-DXA pairs and 99 abdominal CT studies without DXA scores were obtained from an oncology specialty clinic in the Republic of Korea. The center axial CT slices from the L1, L2, L3, and L4 lumbar vertebrae were annotated with the CT slice level and spine segmentation labels for each subject. Deep learning models were trained to localize the center axial slice from the CT scan of the torso, segment the vertebral bone, and estimate BMD for the top four lumbar vertebrae.
Results
Automated vertebra-level DXA measurements showed a mean absolute error (MAE) of 0.079, Pearson’s r of 0.852 (P<0.001), and R² of 0.714. Subject-level predictions on the held-out test set had a MAE of 0.066, Pearson’s r of 0.907 (P<0.001), and R² of 0.781.
Conclusion
CT scans collected during routine examinations without bone densitometry calibration can be used to generate DXA BMD predictions.

Citations

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Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases
Marie Doussiere, Ahlem Aboud, Gilles Dequen, Vincent Goëb
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The Diagnostic Value of Image-Based Machine Learning for Osteoporosis: Systematic Review and Meta-Analysis
Rui Zhao, Haolin Yang, Yangbo Li, Xiaoyun Li, Zhijie Yang, Yanping Lin, Jiachun Huang, Lei Wan, Hongxing Huang
Journal of Medical Internet Research.2026; 28: e75965. CrossRef
Phantomless estimation of bone mineral density on computed tomography: a scoping review
Aleena Waqar, Alberto Bazzocchi, Maria Pilar Aparisi Gómez
RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren.2025; 197(11): 1262. CrossRef
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Sevde Nur Emir, Ahmet Kürşat Soydan, Safiye Sanem Dereli Bulut
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Cheng Zhang, Shanshan Liu, Jialin Shi, Xingyu Zhou, Peter Passias, Nanfang Xu, Weishi Li
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Aobo Wang, Ziqian Ma, Tianyi Wang, Ruiyuan Chen, Yu Xi, Qichao Wu, Shuo Yuan, Ning Fan, Peng Du, Lei Zang
Journal of Medical Internet Research.2025; 27: e77155. CrossRef
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Mengying Xu, Dan Liu, Mengze Zhang, Song Liu, Zhengyang Zhou
BMC Gastroenterology.2025;[Epub] CrossRef
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Mineral, Bone & Muscle
Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm: Sung Hye Kong, Jae-Won Lee, Byeong Uk Bae, Jin Kyeong Sung, Kyu Hwan Jung, Jung Hee Kim, Chan Soo Shin; Endocrinol Metab. 2022;37(4):674-683. Published online August 5, 2022; DOI: https://doi.org/10.3803/EnM.2022.1461

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Background
Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data.
Methods
This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models.
Results
Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women.
Conclusion
DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.

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Hotspots and Trends in the Application of Artificial Intelligence in Spine Medicine from 2005 to 2024: A Bibliometric and Visualization Analysis
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Guangan Luo, Shuanglong Tan, Lincong Luo, Konghe Hu
Frontiers in Medicine.2026;[Epub] CrossRef
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Journal of Clinical Medicine.2026; 15(2): 491. CrossRef
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Shuang Cao, Sihan Yu, Liangbin Huang, Samuel Seery, Yu Xia, Yongwei Zhao, Zhongzhou Si, Xinxue Zhang, Jiqiao Zhu, Ren Lang, Jiantao Kou, Haiming Zhang, Lin Wei, Guangpeng Zhou, Liying Sun, Lei Wang, Ting Li, Qiang He, Zhijun Zhu
Scientific Reports.2025;[Epub] CrossRef
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Mir Sadat-Ali, Bandar A Alzahrani, Turki S Alqahtani, Musaad A Alotaibi, Abdallah M Alhalafi, Ahmed A Alsousi, Abdullah M Alasiri
World Journal of Orthopedics.2025;[Epub] CrossRef
Deep learning-based identification of vertebral fracture and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment to predict incident fracture
Namki Hong, Sang Wouk Cho, Young Han Lee, Chang Oh Kim, Hyeon Chang Kim, Yumie Rhee, William D Leslie, Steven R Cummings, Kyoung Min Kim
Journal of Bone and Mineral Research.2025; 40(5): 628. CrossRef
Aplicaciones de aprendizaje profundo en ortopedia: una revisión sistemática y futuras direcciones
R González-Pola, A Herrera-Lozano, LF Graham-Nieto, G Zermeño-García
Acta Ortopédica Mexicana.2025; 39(3): 152. CrossRef
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O. Venkata Siva, M. S. Anbarasi
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Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen
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Sung Hye Kong
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Sung Hye Kong, Saemee Choi, Wonwoo Cho, Sung Bae Park, Seung Shin Park, Jaegul Choo, Jung Hee Kim, Sang Wan Kim, Chan Soo Shin
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Li-Wei Cheng, Hsin-Hung Chou, Yu-Xuan Cai, Kuo-Yuan Huang, Chin-Chiang Hsieh, Po-Lun Chu, I-Szu Cheng, Sun-Yuan Hsieh
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Baisen Chen, Jiaming Cui, Chaochen Li, Pengjun Xu, Guanhua Xu, Jiawei Jiang, Pengfei Xue, Yuyu Sun, Zhiming Cui
Journal of Orthopaedic Research.2024; 42(6): 1356. CrossRef
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Satoshi Maki, Takeo Furuya, Masahiro Inoue, Yasuhiro Shiga, Kazuhide Inage, Yawara Eguchi, Sumihisa Orita, Seiji Ohtori
Journal of Clinical Medicine.2024; 13(3): 705. CrossRef
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Yisak Kim, Young-Gon Kim, Jung-Wee Park, Byung Woo Kim, Youmin Shin, Sung Hye Kong, Jung Hee Kim, Young-Kyun Lee, Sang Wan Kim, Chan Soo Shin
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Yu He, Jiaxi Lin, Shiqi Zhu, Jinzhou Zhu, Zhonghua Xu
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Xuetao Zhu, Dejian Liu, Lian Liu, Jingxuan Guo, Zedi Li, Yixiang Zhao, Tianhao Wu, Kaiwen Liu, Xinyu Liu, Xin Pan, Lei Qi, Yuanqiang Zhang, Lei Cheng, Bin Chen
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Sungwon Lee, Joon-Yong Jung, Akaworn Mahatthanatrakul, Jin-Sung Kim
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Yue Li, Zhuang Liang, Yingchun Li, Yang Cao, Hui Zhang, Bo Dong
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Guillaume Gatineau, Enisa Shevroja, Colin Vendrami, Elena Gonzalez-Rodriguez, William D Leslie, Olivier Lamy, Didier Hans
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Srikar R. Namireddy, Saran S. Gill, Amaan Peerbhai, Abith G. Kamath, Daniele S. C. Ramsay, Hariharan Subbiah Ponniah, Ahmed Salih, Dragan Jankovic, Darius Kalasauskas, Jonathan Neuhoff, Andreas Kramer, Salvatore Russo, Santhosh G. Thavarajasingam
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Min Wook Joo, Taehoon Ko, Min Seob Kim, Yong-Suk Lee, Seung Han Shin, Yang-Guk Chung, Hong Kwon Lee
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Sophia S. Goller, Jon F. Rischewski, Thomas Liebig, Jens Ricke, Sebastian Siller, Vanessa F. Schmidt, Robert Stahl, Julian Kulozik, Thomas Baum, Jan S. Kirschke, Sarah C. Foreman, Alexandra S. Gersing
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Kainat A. Ullah, Faisal Rehman, Muhammad Anwar, Muhammad Faheem, Naveed Riaz
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Zhihao Su, Afzan Adam, Mohammad Faidzul Nasrudin, Masri Ayob, Gauthamen Punganan
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Sang Won Jo, Eun Kyung Khil, Kyoung Yeon Lee, Il Choi, Yu Sung Yoon, Jang Gyu Cha, Jae Hyeok Lee, Hyunggi Kim, Sun Yeop Lee
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Anthony K. Allam, Adrish Anand, Alex R. Flores, Alexander E. Ropper
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Jeonghoon Ha
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Review Articles

Miscellaneous
Machine Learning Applications in Endocrinology and Metabolism Research: An Overview: Namki Hong, Heajeong Park, Yumie Rhee; Endocrinol Metab. 2020;35(1):71-84. Published online March 19, 2020; DOI: https://doi.org/10.3803/EnM.2020.35.1.71

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Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs, and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, and interpret data with a combination of clinical expertise and scientific rigor.

Citations

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Enhancing diagnostic accuracy of thyroid nodules: integrating self-learning and artificial intelligence in clinical training
Daham Kim, Yoon-a Hwang, Youngsook Kim, Hye Sun Lee, Eunjung Lee, Hyunju Lee, Jung Hyun Yoon, Vivian Youngjean Park, Miribi Rho, Jiyoung Yoon, Si Eun Lee, Jin Young Kwak
Endocrine.2025; 88(3): 766. CrossRef
Harnessing machine learning for improved diagnosis, drug discovery, and patient care
Jibon Kumar Paul, Mahir Azmal, Omar Faruk Talukder, ANM Shah Newaz Been Haque, Meghla Meem, Ajit Ghosh
Computational and Structural Biotechnology Reports.2025; 2: 100051. CrossRef
Revolutionizing healthcare with 5 G and AI: Integrating emerging technologies for personalized care and cancer management
Abhishek Kumar, Jagat Pal Yadav, Shubhrat Maheshwari, Aditya Singh, Vineet Srivastava, Habibullah Khalilullah, Amita Verma
Intelligent Hospital.2025; 1(1): 100005. CrossRef
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Mohamed Zaim Wadghiri, Ali Idri
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Sanjay Dhanka, Ankur Kumar, Abhinav Sharma, Haswanth Vundavilli, Surita Maini, Elakkiya Rajasekar
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Min Chen, Yuxin Li, Sumei Zhou, Linbo Zou, Lei Yu, Tianfang Deng, Xian Rong, Shirong Shao, Jijun Wu
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Application of artificial intelligence in ultrasound diagnostics of thyroid nodules
E. A. Troshina, S. M. Zakharova, K. V. Tsyguleva, I. A. Lozhkin, D. V. Korolev, A. A. Trukhin, K. S. Zaytsev, T. V. Soldatova, A. A. Garmash
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Miscellaneous
Medical Big Data Is Not Yet Available: Why We Need Realism Rather than Exaggeration: Hun-Sung Kim, Dai-Jin Kim, Kun-Ho Yoon; Endocrinol Metab. 2019;34(4):349-354. Published online December 23, 2019; DOI: https://doi.org/10.3803/EnM.2019.34.4.349

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Abstract PDF PubReader ePub

Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.

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