Look～今天是小红

A considerable proportion of patients with T1 high-grade non-muscle invasive bladder cancer (NMIBC) advance to muscle-invasive disease, and the disease has a high rate of recurrence. Numerous approaches have been proposed in the literature to mitigate the consequences of the initial transurethral resection of bladder tumors (TURBT). We aimed to evaluate the T1 high-grade bladder tumor by restaging transurethral resection.

We propose a theory and methodology for designing human-artificial intelligence (AI) collaboration in complex, embodied tasks. The theory distinguishes human embodied intelligence from computational intelligence and identifies synergies in which AI enhances-rather than replicates or replaces-human performance. We represent observable structures of expert performance as a nested network with four interdependent layers: