| Sumario: | Simple Summary: Artificial intelligence (AI) is transforming urological oncology surgery. While earlier systems were mainly limited to analyzing image data, modern multimodal models can combine various sources of information, such as imaging, laboratory values, robotic data and clinical documentation. This allows for more precise diagnostics, surgical planning and follow-up care. Generative AI can also enhance communication between physicians and patients, as well as support surgical training by providing realistic simulations and personalized feedback. However, there are risks associated with incomplete or distorted training data, data protection issues and unclear responsibilities in AI-supported decision-making. A better understanding of the opportunities and limitations of multimodal and generative AI is crucial to ensure its safe, ethical and clinically meaningful use in urological practice. Background: Multimodal artificial intelligence (MMAI) is transforming urological oncology by enabling the seamless integration of diverse data sources, including imaging, clinical records and robotic telemetry to facilitate patient-specific decision-making. Methods: This narrative review summarizes the current developments, applications, opportunities and risks of multimodal AI systems throughout the entire perioperative process in uro-oncologic surgery. Results: MMAI demonstrates quantifiable benefits across the entire perioperative pathway. Preoperatively, it improves diagnostics and surgical planning via multimodal data fusion. Intraoperatively, AI-assisted systems provide real-time context-based decision support, risk prediction and skill assessment within the operating theater. Postoperatively, MMAI facilitates automated documentation, early complication detection and personalized follow-up. Generative AI further revolutionizes surgical training through adaptive feedback and simulations. However, critical limitations must be addressed, including data bias, the barrier of closed robotic platforms, insufficient model validation, data security issues, hallucinations and ethical concerns regarding liability and transparency. Conclusions: MMAI significantly enhances the precision, efficiency and patient-centeredness of uro-oncological care. To ensure safe and widespread implementation, resolving the technical and regulatory barriers to real-time integration into robotic platforms is paramount. This must be coupled with standardized quality controls, transparent decision-making processes and responsible integration that fully preserves physician autonomy.
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