Digital twins for personalized treatment in uro-oncology in the era of artificial intelligence.

'Digital twins', also called 'digital patient twins' or 'virtual human twins' - digital patient-specific models derived from multimodal health data - are a strong focus in health care and are emerging as a promising tool for improving personalized care in uro-oncology. These models can integrate clinical, genomic, imaging and histopathological information to simulate organ behaviour and disease progress as well as predict responses to treatments. The concept of digital twins has shown potential in various fields, but its application in uro-oncology is still evolving, with few assessments of their feasibility and clinical utility. The advent of artificial intelligence adds a new dimension to their development, potentially enabling the synthesis of diverse, high-quality datasets to improve modelling accuracy and support real-time decision-making. However, substantial challenges exist, including data integration, patient privacy, computational demands and ethical frameworks. In addition, the interpretability of predictions remains essential for gaining clinical trust and guiding patient-centred decisions. The use of digital twins in uro-oncology has the potential to improve patient stratification and treatment planning; however, barriers must be overcome for their successful implementation in clinical routine. By integrating new technologies, fostering interdisciplinary collaboration and prioritizing transparency, digital twins could shape the future of precision uro-oncology.