A review of Artificial Intelligence methods in bladder cancer: segmentation, classification, and detection
Ayah Bashkami, Ahmad Nasayreh, Sharif Naser Makhadmeh, Hasan Gharaibeh, Ahmed Ibrahim Alzahrani, Ayed Alwadain, Jia Heming, Absalom E. Ezugwu, Laith Abualigah
Abstract
Abstract Artificial intelligence (AI) and other disruptive technologies can potentially improve healthcare across various disciplines. Its subclasses, artificial neural networks, deep learning, and machine learning, excel in extracting insights from large datasets and improving predictive models to boost their utility and accuracy. Though research in this area is still in its early phases, it holds enormous potential for the diagnosis, prognosis, and treatment of urological diseases, such as bladder cancer. The long-used nomograms and other classic forecasting approaches are being reconsidered considering AI’s capabilities. This review emphasizes the coming integration of artificial intelligence into healthcare settings while critically examining the most recent and significant literature on the subject. This study seeks to define the status of AI and its potential for the future, with a special emphasis on how AI can transform bladder cancer diagnosis and treatment.