Integration of Artificial Intelligence into Endocrinology Teaching: Opportunities, Challenges and Responses
Journal: Journal of Clinical Medicine Research DOI: 10.32629/jcmr.v6i4.4674
Abstract
Endocrinology is a crucial discipline in medical education, and its teaching quality has a significant impact on the cultivation of medical students' clinical diagnosis and treatment capabilities. However, the traditional teaching model has several issues, such as limited course time, uneven distribution of teaching resources, and a single teaching mode. With technological innovations like deep learning algorithms, natural language processing, and virtual reality, artificial intelligence (AI) technology has provided entirely new ideas for the reform of endocrinology teaching. This paper systematically explores the application of AI in endocrinology teaching, thoroughly analyzes the current status and challenges of endocrinology teaching, elaborates on the advantages and specific application methods of AI, and simultaneously proposes targeted countermeasures for the challenges faced during the application process. The aim is to promote the innovative development of endocrinology teaching through AI technology, improve teaching quality, cultivate more high - quality endocrinology professionals, and contribute to the development of medical education.
Keywords
artificial intelligence; endocrinology; medical teaching; clinical practice; knowledge system
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[3]Chen Feng, Huang Guozhen, Zhuge Jing, et al. International Landscape and Trends of Artificial Intelligence in Medical Education Applications. Medicine & Philosophy, 2024, 45(2): 67-71, 81.
[4]Sheng B, Pushpanathan K, Guan Z, et al. Artificial intelligence for diabetes care: current and future prospects[J]. Lancet Diabetes Endocrinol. 2024,12(8):569–595.
[5]Huang Guozhen, Fang Jianwen, Tu Yunfang. Global Landscape and Trends of Artificial Intelligence Education Applications. Modern Distance Education Research, 2022, 34(3):3-14.
[6]Wang Mengxi, Wang Na, Zhang Xinduo, et al. Construction of Artificial Intelligence Medical Teaching Platform. China Higher Medical Education, 2020, 3:46-48.
[7]Assié G, Allassonnière S. Artificial Intelligence in Endocrinology: On Track Toward Great Opportunities[J]. J Clin Endocrinol Metab. 2024,109(6):e1462-e1467.
[8]Magrabi F, Ammenwerth E, McNair JB，et al．Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications[J]. Yearb Med Inform. 2019,28(1):128-134.
[9]Gruson D, Dabla P, Stankovic S, et al. Artificial intelligence and thyroid disease management: considerations for thyroid function tests[J]. Biochem Med (Zagreb). 2022,32(2):20601.
[10]Bhattacharya S, Mahato RK, Singh S, et al．Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches[J]. Life Sci. 2023,332:122110.
[11]Langford MA, Chan KH, Fleck JE, et al．MoDALAS: addressing assurance for learning-enabled autonomous systems in the face of uncertainty[J]. Softw Syst Model. 2023,18:1-21.
[12]Wändell P, Carlsson AC, Wierzbicka M, et al. A machine learning tool for identifying patients with newly diagnosed diabetes in primary care[J]. Prim Care Diabetes. 2024,18(5):501–505.
[13]Alkattan A, Al-Zeer A, Alsaawi F, et al. The utility of a machine learning model in identifying people at high risk of type 2 diabetes mellitus[J]. Expert Rev Endocrinol Metab. 2024,19(6):513-522.
[14]Barteit S, Lanfermann L, Bärnighausen T, et al．Augmented, Mixed, and Virtual Reality-Based Head-Mounted Devices for Medical Education: Systematic Review[J]. JMIR Serious Games. 2021,9(3):e29080.
[15]Guo Chenxi, Ou Fengrong. Application Progress of Virtual Reality Technology in Clinical Practice Teaching. Shenyang Medical College Journal, 2021, 23(6):513-516,521.
[16]Nagi F, Salih R, Alzubaidi M, et al．Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review[J]. Stud Health Technol Inform. 2023,305:648-651.
[17]Zahra MA, Al-Taher A, Alquhaidan M, et al. The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease[J]. Drug Metab Pers Ther. 2024,39(2):47–58.
[18]Zhang Xuan, Chen Qi, Wang Jiake, et al. Research on Empowering Continuing Education Reform for General Practitioners with Artificial Intelligence. China Continuing Medical Education, 2024, 14:18-21.
[19] Song Chao, Zhang Wen, Hong Yunxia, et al. Case-based Teaching Exploration of the "Artificial Intelligence and Medical Applications" The Guide of Science & Education, 2023, 27:107-109.
[20]Zhang Xiaoqin, Tan Liwen, Wu Yi, et al. Case-based Teaching and Practice of Artificial Intelligence Courses for Medical Undergraduates.Chongqing Medical Journal, 2020, 49(13):2226-2228.
[21]Feng Jia, Zheng Linlin, Zhang Yuping, et al. The Application Range of Virtual Reality Technology in Medical Emergency Skills Training. Chinese Journal of Emergency Medicine, 2024, 33(6):857-862.
[22]Wojtara M, Rana E, Rahman T, et al. Artificial intelligence in rare disease diagnosis and treatment[J]. Clin Transl Sci. 2023,16(11):2106-2111.
[23]Hasanzad M, Aghaei Meybodi HR, Sarhangi N, et al. Artificial intelligence perspective in the future of endocrine diseases[J]. J Diabetes Metab Disord. 2022,21(1):971–978.
[24]Wang Yan, Chu Hong, Liang Jingjing, et al. The Current Situation and Exploration of Higher Medical Education Reform under the Background of Artificial Intelligence. China Medical Herald, 2022, 19(13):64-67.
[25]Gupta NS, Kumar P.Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine[J]. Comput Biol Med. 2023,162:107051.
Copyright © 2025 Ruobei Zhao, Longbing Lai, Zehua Zhou, Jiangang Lu, Fengning Chuan
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