ARTIFICIAL INTELLIGENCE IN EARLY DETECTION AND MANAGEMENT OF BENIGN UROLOGICAL TUMORS-A NARRATIVE REVIEW

Abstract

Background: Benign urological tumors of the kidney and bladder, such as renal oncocytomas, angiomyolipomas, and non-muscle invasive bladder lesions, are increasingly diagnosed due to widespread use of imaging modalities. Differentiating these lesions from malignant counterparts remains a clinical challenge, often leading to unnecessary interventions. Artificial intelligence (AI) has emerged as a transformative tool in urology, offering advanced diagnostic support through radiomics, machine learning, and image analysis. Objective: This narrative review aims to explore the current applications, limitations, and future potential of AI in the early detection and management of benign kidney and bladder tumors, focusing on its integration within medical imaging and urological practice. Main Discussion Points: Key themes include the role of AI-enhanced radiological imaging in differentiating benign from malignant renal masses, AI-guided cystoscopy and histopathology for improved bladder tumor assessment, predictive analytics for individualized surveillance, and intraoperative AI tools for surgical planning. The review also discusses limitations such as small sample sizes, methodological variability, and issues related to generalizability and data standardization. Conclusion: AI shows promising potential in improving diagnostic accuracy and clinical decision-making in benign urological tumors. However, the evidence base is still developing and limited by methodological weaknesses. Larger, multicenter prospective studies are necessary to validate current findings and support safe, widespread clinical implementation.

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