## 4 Discussion
This new technology is also usable in open surgery [87], in simulation and training [88]. In addition to urological surgery [89], it is also used in hepatobiliary [90; 91; 92], otolaryngology [93; 94] or orthopedic surgery [95]. [PERSON] [54; 55; 56] has developed an interesting tool combining robotics and AI. By recording the surgeon's movements while operating, AI provide automated performance metrics and determine global movement features [96; 97; 98]. ML now allows the extraction and exploitation of surgical skills. With these data, new opportunities are opening. It is now feasible to evaluate surgical skills, train them efficiently, establish as yet unknown correlations with post-operative or oncologic outcomes, and set a new standardization of practices [99]. These new discoveries are achieved by the ability to recognize complex patterns through nonlinear relationships, with high accuracy, even when data are missing or contaminated [100]. These methods raise some concerns and bring up issues. First, to be legitimate or acceptable, or even to be rejected as irrelevant, a ML algorithm's decisions must be understandable and therefore explainable. The number of micro-thought processes carried out by the system is beyond our understanding capacities. As previously stated, there is a hidden layer of neurons in ANN software. Therefore, calculations that result in outcomes are not visible for the programmer who created ANNs, responsible for opacity [3; 101]. It is the black-box effect. The data goes in the black box and the prediction comes out, but without any accompanying justification [102]. It is the difference between prediction and explanation. Failure to understand the algorithm decision can lead to misinterpretation. One other concern is related to the training sample used to propose ML algorithm. To be functional, a ML algorithm needs a training step, using usually a training sample from the study population. However, this sample, which is often limited in size, may not represent the general population. In a systematic review of skin cancer classification using CNN, [PERSON] [16] highlights the lack of dark-skinned people in the different training samples to be representative and be applied on world population. For PCa, it is known that ethnic origin is source of some predisposition. PCa does not have the same characteristics and severity in an Afro-American or Asian population [103]. Yet, most of the previously reviewed studies are based on Asian populations [20; 26; 28; 37; 42; 50; 59] and are probably not applicable to an African American population. [PERSON] [63] emphasizes this difference by showing that Afro-American patients in their study were clustered at the upper end of the pain index spectrum, even if these results were not significant. As already mentioned, a ML algorithm performance is related to the training step, cohort parameters, and optimization processes. Each model is therefore different from another, created and trained with another set of data. In addition, programmers and researchers often keep to themselves their code source code and data. There is an emergence of many AI models, which differs in the functioning, with complicated reproducibility to establish. Therefore, there is a critical need to propose external validation at these algorithms before any use in daily practice. In addition to this lack of external validation, AI algorithms are insufficiently compared to current references and comparing MLsoftware with human experts is not a common practice [104], even in PCa. In this review, only 6 studies have been compared to current reference prediction methods, such as Partin tables [105,106], or expert opinion [44,47,48]. Therefore, the comparison between AI and human experts is still underdeveloped in PCa while in other fields, AI has achieved a confirmed expert level such as in classification of skin cancer [107], retinal diseases [108,109], or colorectal polype classification [110,111].