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REVIEW article

Front. Oncol.
Sec. Genitourinary Oncology
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1487676
This article is part of the Research Topic The Role of AI in GU Oncology View all 4 articles

Artificial intelligence application in the diagnosis and treatment of bladder cancer: Advance, challenges, and opportunities

Provisionally accepted
Hongzhou Cai Hongzhou Cai 1*Xiaoyu Ma Xiaoyu Ma 1*Qiuchen Zhang Qiuchen Zhang 2*Lvqi He Lvqi He 2*Xinyang Liu Xinyang Liu 2*Yang Xiao Yang Xiao 1*Jingwen Hu Jingwen Hu 3*Shengjie Cai Shengjie Cai 4*Bin Yu Bin Yu 1*
  • 1 Department of Urology, Jiangsu Cancer Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
  • 2 Fourth School of Clinical Medicine, Nanjing Medical Universtiy, Nanjing, Jiangsu Province, China
  • 3 School of Public Health, Southern Medical University, Guangzhou, China
  • 4 Third Clinical Medical School, Nanjing University of Chinese Medicine, Yancheng, China

The final, formatted version of the article will be published soon.

    Bladder cancer (BC) is a serious and common malignant tumor of the urinary system. Accurate and convenient diagnosis and treatment of BC is a major challenge for the medical community. Due to the limited medical resources, the existing diagnosis and treatment protocols for BC without the assistance of artificial intelligence (AI) still have certain shortcomings. In recent years, with the development of AI technologies such as deep learning and machine learning, the maturity of AI has made it more and more applied to the medical field, including improving the speed and accuracy of BC diagnosis and providing more powerful treatment options and recommendations related to prognosis. Advances in medical imaging technology and molecular-level research have also contributed to the further development of such AI applications. However, due to differences in the sources of training information and algorithm design issues, there is still room for improvement in terms of accuracy and transparency for the broader use of AI in clinical practice. With the popularization of digitization of clinical information and the proposal of new algorithms, artificial intelligence is expected to learn more effectively and analyze similar cases more accurately and reliably, promoting the development of precision medicine, reducing resource consumption, and speeding up diagnosis and treatment. This review focuses on the application of artificial intelligence in the diagnosis and treatment of BC, points out some of the challenges it faces, and looks forward to its future development.

    Keywords: Bladder cancer1, Artificial Intelligence2, Deep learning3, machine learning4, radiation therapy5

    Received: 28 Aug 2024; Accepted: 16 Oct 2024.

    Copyright: © 2024 Cai, Ma, Zhang, He, Liu, Xiao, Hu, Cai and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Hongzhou Cai, Department of Urology, Jiangsu Cancer Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
    Xiaoyu Ma, Department of Urology, Jiangsu Cancer Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
    Qiuchen Zhang, Fourth School of Clinical Medicine, Nanjing Medical Universtiy, Nanjing, 210029, Jiangsu Province, China
    Lvqi He, Fourth School of Clinical Medicine, Nanjing Medical Universtiy, Nanjing, 210029, Jiangsu Province, China
    Xinyang Liu, Fourth School of Clinical Medicine, Nanjing Medical Universtiy, Nanjing, 210029, Jiangsu Province, China
    Yang Xiao, Department of Urology, Jiangsu Cancer Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China
    Jingwen Hu, School of Public Health, Southern Medical University, Guangzhou, 510515, China
    Shengjie Cai, Third Clinical Medical School, Nanjing University of Chinese Medicine, Yancheng, 211166, China
    Bin Yu, Department of Urology, Jiangsu Cancer Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.