## 7 Discussion  
This paper aimed to analyze the latest achievements of Artificial Intelligence in the field of orthopedics, providing the reader with an overview of all the possibilities of this technology. As shown, machine learning models can indeed provide meaningful assistance to surgeons in their decisions or even help in diagnosing a certain pathology.  
For the purpose of diagnosing, different studies were analyzed in which models were trained to recognize fractures in various parts of the body such as the hip and the knee. Other models, instead, were trained to improve time-consuming tasks such as MRI image improvement or implant model and characteristics recognition. The models were able to perform the tasks similarly if not superiorly to their human counterpart, according to performance indexes as accuracy, sensitivity, specificity, precision, F1-score and AUC. Admirable results were thus achieved, but it must be kept in mind that most of the time the models had to deal only with a simple binary recognition (as an example, \"fracture is present\" versus \"no fracture found\") and therefore they were not able to perform specific and exhaustive diagnosis.  
Regarding prediction, instead, in the cited studies various ML models were trained to predict different aspects related to surgery such as the following: length of stay after TKA surgery, evaluation of suitability of a candidate for TKA or UKA based on three different views of the knee joint, surgery cost increase for patients with comorbidities with respect to patients with only the primary condition, and post-operative complications after total knee or hip arthroplasties. These tasks were performed satisfyingly as well, and some models were even externally validated (thus assessing the generalizability of their algorithm).  
It is to keep in consideration that all the models addressed are strictly dependent on the data fed to them, which should therefore be of the right quantity and also of the right quality. The data fed to the algorithm should be unbiased, hence gathered and selected by competent figures able to ensure a proper representativeness of the general population. No discrimination should be permitted. Since the machine can amplify the intrinsic biases of the data, moreover, the data selected to train the model should be considered responsible for the results given by the machine. This theme of responsibility in medical decision-making is crucial and for this reason is the main topic of current scientifical discussions:who would be responsible for the mistake in the decision-making process once man and machine cooperate is indeed still not clear, and inherent laws are missing.  
All of the models were tested and evaluated on their performance, but their integration in the medical environment was not assessed: this means also that the interaction between clinician and machine was not analyzed, together with the impact of such a technology on day-to-day work. What was instead considered in some of the addressed studies is that a strict collaboration between the clinician and a data scientist is fundamental, since the medical figure alone would need knowledge and skills in a field different from their own in order to maintain the machine and validate the results given by the model.  
It is still finally to be kept in consideration the fear that, while these models are still far from substituting the human clinician, they could already interfere in the patient-clinician interaction, which is crucial for the correct evaluation of the patient's situation and for the decision-making process. Moreover, the clinicians could over time rely less and less on their competence and skills as they become more complacent about the results given by the machine, instead of analyzing them with a critical approach.