Renal cancer (RC) ranks tenth among the most frequently diagnosed cancers and affects both men and women worldwide. This disease is a significant global health issue, highlighting the need for accurate and rapid diagnostic tools to guide treatment. Conventional pathological methods have drawbacks, such as extended evaluation procedures and inter-observer inconsistency. Recent developments in artificial intelligence (AI) have enabled the progress of AI-powered computer-assisted diagnostic and predictive systems for various diseases, including cancer. A comprehensive literature review examined the latest advancements in AI and RC technologies. Advanced image analysis methods enable AI systems to measure molecular and cellular markers, thereby improving the precision and reproducibility of RC detection. This narrative review highlights the basic ideas and comprehensively summarizes modern AI methods for RC. Early clinical outcome prediction, renal carcinoma subtyping, grading, staging, and disease identification are only areas in which their potential has been demonstrated. Before applying this in daily practice, healthcare practitioners must understand the fundamentals and interact across different fields to standardize datasets, establish relevant outcomes, and merge interpretations.
Walsh et al. (Tue,) studied this question.