Artificial Intelligence in the Detection, Characterization, and Management of Renal Masses: A Narrative Review.

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Abstract

Kidney tumors are being found more often today because CT and MRI scans are widely used and kidney masses are frequently discovered by chance. Many of these masses are benign. However, most patients still undergo surgery because doctors cannot confirm the diagnosis from imaging alone. Artificial intelligence (AI) refers to computer systems that perform tasks normally requiring human intelligence, including machine learning, in which algorithms learn patterns directly from data, and radiomics, in which quantitative features are extracted from medical images to support diagnosis. These tools offer new ways to improve how kidney masses are detected, characterized, and treated. This narrative review summarizes current evidence on AI applications across the renal mass pathway, covering automated detection, imaging-based characterization, pathological staging, prognosis prediction, and AI-assisted surgical planning, while also outlining current limitations and future research directions. A review of the English-language literature was performed using PubMed and MEDLINE, with studies published between 2018 and 2026 prioritized. AI shows strong performance across all stages of the renal mass pathway. Deep learning models accurately detect and segment renal masses on CT and MRI. Radiomics-based classifiers distinguish benign from malignant lesions and predict tumor subtype without biopsy. Multimodal AI models predict survival with high accuracy and outperform established clinical scoring systems. AI-assisted surgical planning tools support nephron-sparing surgery and predict postoperative kidney function. Wider clinical use requires better prospective validation, more diverse datasets, and improved model transparency.

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