The Application of Deep Learning in the Medical Field
DOI:
https://doi.org/10.54097/wpfkw443Keywords:
Deep learning; medical field; disease.Abstract
The medical system is confronted with the dual challenges of a bottleneck in diagnostic efficiency and a surging demand for personalized treatment. Meanwhile, the traditional medical model has demonstrated significant limitations in the early detection of complex diseases and the stratification of heterogeneous patient populations. In the context of digital transformation in healthcare and the growing demand for precision medicine, deep learning has emerged as a pivotal force in overcoming traditional medical limitations through its robust data processing and pattern recognition capabilities. This paper systematically examines the application logic of deep learning in the medical field, elucidates its fundamental principles, and discusses its value in advancing healthcare development. The research provides an in-depth analysis of core technologies, introduces classical algorithm systems, and highlights how deep learning enhances pharmaceutical research and development (R&D). It further details how deep learning improves the accuracy of medical imaging analysis and boosts disease prediction precision. Not only does this study reveal how deep learning is reshaping the medical ecosystem and driving the implementation of precision medicine, but it also offers theoretical and practical references for intelligent development in the healthcare industry.
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