Version 1
: Received: 18 August 2024 / Approved: 19 August 2024 / Online: 19 August 2024 (09:54:35 CEST)
How to cite:
Chowdhury, A. T.; Salam, A.; Naznine, M.; Abdalla, D.; Erdman, L.; Chowdhury, M. E.; Abbas, T. O. Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances. Preprints2024, 2024081307. https://doi.org/10.20944/preprints202408.1307.v1
Chowdhury, A. T.; Salam, A.; Naznine, M.; Abdalla, D.; Erdman, L.; Chowdhury, M. E.; Abbas, T. O. Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances. Preprints 2024, 2024081307. https://doi.org/10.20944/preprints202408.1307.v1
Chowdhury, A. T.; Salam, A.; Naznine, M.; Abdalla, D.; Erdman, L.; Chowdhury, M. E.; Abbas, T. O. Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances. Preprints2024, 2024081307. https://doi.org/10.20944/preprints202408.1307.v1
APA Style
Chowdhury, A. T., Salam, A., Naznine, M., Abdalla, D., Erdman, L., Chowdhury, M. E., & Abbas, T. O. (2024). Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances. Preprints. https://doi.org/10.20944/preprints202408.1307.v1
Chicago/Turabian Style
Chowdhury, A. T., Muhammad E.H. Chowdhury and Tariq O. Abbas. 2024 "Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances" Preprints. https://doi.org/10.20944/preprints202408.1307.v1
Abstract
Artificial intelligence (AI) is transforming many healthcare fields including pediatric urology. This in-depth review explores recent advances in AI tools for pediatric urology, emphasizing how state-of-the-art technologies can be used to enhance patient treatment. An initial overview of pediatric urology is given to provide context for this interdisciplinary field and the wide spectrum of problems this entails, before discussing potential uses of AI to improve diagnostic accuracy, treatment planning, and surgical results. AI-powered predictive models are already changing pediatric clinical practice by guiding interpretation of medical images and supporting decision making. In addition to outlining the aims, methods, and conclusions of previous studies on AI applications in pediatric urology, this review also highlights knowledge gaps and priority areas for future study. Particular focus is given to specialized surgeries such pyeloplasty, where AI-guided treatment may help overcome specific technical challenges of this complex procedure. Also highlighted are practical, ethical, and legal aspects of integrating AI into pediatric urology, which will be crucial for responsible innovation in patient-centered treatment. Overall, this article aims to enlighten and inspire clinicians, researchers, and other healthcare stakeholders by offering insight into the future of AI in pediatric urology.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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