A review of Artificial Intelligence methods in bladder cancer: segmentation, classification, and detection

• Published in: The Artificial intelligence review Vol. 57; no. 12; p. 339
• Main Authors: Bashkami, Ayah, Nasayreh, Ahmad, Makhadmeh, Sharif Naser, Gharaibeh, Hasan, Alzahrani, Ahmed Ibrahim, Alwadain, Ayed, Heming, Jia, Ezugwu, Absalom E., Abualigah, Laith
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 21.10.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Biopsy; Bladder; Bladder cancer; Cancer; Computer Science; Datasets; Deep learning; Diagnosis; Disease; Efficiency; Health care; Health services; Histopathology; Machine learning; Medical diagnosis; Neural networks; Prediction models; Deep learning; Bladder Cancer; Histopathology; Artificial Intelligence; Machine learning
• ISSN: 1573-7462; 0269-2821; 1573-7462
• DOI: 10.1007/s10462-024-10953-6

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.