Explore the Current Application Status of Machine Learning in the Medical Field

Zhe Zhang

doi:10.47297/taposatWSP2633-456901.20250602

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Explore the Current Application Status of Machine Learning in the Medical Field

Volume 6, Issue 2

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Explore the Current Application Status of Machine Learning in the Medical Field
Explore the Current Application Status of Machine Learning in the Medical Field
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China
Vol. 6, Issue 2, Pages: 5-8(2025)
Published：25 February 2025
DOI：10.47297/taposatWSP2633-456901.20250602
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Zhang Zhe.Explore the Current Application Status of Machine Learning in the Medical Field[J].Theory and Practice of Science and Technology,2025,06(02):5-8.
DOI：

Zhang Zhe.Explore the Current Application Status of Machine Learning in the Medical Field[J].Theory and Practice of Science and Technology,2025,06(02):5-8. DOI： 10.47297/taposatWSP2633-456901.20250602.

摘要

Abstract

The application of machine learning in the medical field can improve the quality and efficiency of medical services and promote the innovative development of healthcare. However

there are still many shortcomings in the development of related fields. This paper explores the current development status of machine learning technology and its application in the medical field and explores the direction of its future development and the defects that can be improved

mainly through the literature research method. This paper finds that many current studies lack mature large-scale datasets as training materials. Similarly

the trained models lack applications in real-world scenarios to validate their prediction or detection performance

and can only be verified in other existing datasets

which requires wider collaboration to enable and improve them. The significance of this study mainly lies in sorting out the current development status of machine learning applications in the medical field in China

illustrating the breadth and limitations of the current applications of AI in the medical field

and providing references and guidance for the future development of related fields.

关键词

Keywords

references

X. Liu, Construction of drug prediction model for treatment of diabetes mellitus based on machine learning. Master's thesis, University of Electronic Science and Technology (2024).

Z. Hu, S. Zhang, Y. Chen, S. Liu, F. Liu, Y. Chen, Advances in machine learning and deep learning in smoke disease imaging. J. Clin. Neurosurg. (5), 581-583,587 (2024).

W. Jiang, X.J. Chang, F. Zeng, Y.P. Lan, Current status of artificial intelligence in prognostic prediction modeling for slow plus acute liver failure. J. Clin. Hepatobiliary Dis. (9), 1891-1896 (2024).

L. Tian, O. Ren, Z. Tu, Advances in the application of artificial intelligence, machine learning, and deep learning in medical diagnosis. Mod. Med. (9), 1480-1484 (2024).

J.L. Li, X. Liu, Q.Y. Lin, H.-R. Long, C.H. Jiang, Y.Y. Huo, Current status of deep learning in screening lung diseases by chest radiography. Chin. J. Anti-Tuberculosis 1-18 (2024).

S. Zhang, M. Chen, Z. Fang, W. Tu, Construction and exploration of heart disease prediction model based on machine learning. Hosp. Manag. Forum (5), 58-61 (2024).

Q.W. Tseng, Research on artificial intelligence model for staging diagnosis and lymph node metastasis prediction of early stage gastric cancer. Ph.D. thesis, Nanchang University (2024).

Z.F. Peng, H. Xu, B. Yang, Study on the application of CT deep learning and image histology in individualized treatment of non-small cell lung cancer. Chin. J. Integr. Imaging (6), 1-6 (2024).

P. Yang, Research on automatic segmentation algorithm of tumor target area for nasopharyngeal carcinoma radiation therapy. Master's thesis, Sichuan University (2021).

H. Lu, Intelligent comprehensive data analysis function and implementation of medical information management system. Internet Weekly (12), 38-40 (2024).

J. Wen, Clinical feature-driven intelligent assisted imaging histomics research. Ph.D. thesis, Guangzhou University (2024).

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