A deep learning model demonstrated promising diagnostic performance in differentiating acute scrotal emergencies using single US images. Its feasibility was preliminarily assessed in a small pilot study. These findings support further investigation, with larger and more balanced multicentre studies required to establish clinical utility and effective workflow integration.

Combination therapy with mirabegron and tamsulosin is more effective than monotherapy in improving LUTS in patients with BPH, particularly storage symptoms, with a comparable safety profile. A fixed-dose combination formulation in the future may further improve patient adherence and quality of life.

A deep learning model demonstrated promising diagnostic performance in differentiating acute scrotal emergencies using single US images. Its feasibility was preliminarily assessed in a small pilot study. These findings support further investigation, with larger and more balanced multicentre studies required to establish clinical utility and effective workflow integration.

Combination therapy with mirabegron and tamsulosin is more effective than monotherapy in improving LUTS in patients with BPH, particularly storage symptoms, with a comparable safety profile. A fixed-dose combination formulation in the future may further improve patient adherence and quality of life.

We demonstrate the potential effectiveness of deep learning segmentation in robotic prostatectomy procedures. Deep learning algorithms could be used to identify anatomical structures within the surgical field and may provide real-time guidance in robotic surgery.

Deep learning models provided accurate segmentation of the surgical instruments and anatomical structures observed during RARP. Both CNN and transformer models showed reliable predictions in the segmentation task; however, CNN models may be more suitable than transformer models for organ segmentation and may be more applicable in unusual cases. Further research with large datasets is needed.

ClinicalTrials.gov NCT03572231.

AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.