Breast cancer is a real public health problem in Morocco. It is the cause of a significant number of deaths caused by late diagnosis. Mammography plays an essential role in the detection of breast cancer and in the early management of its treatment. Despite the existence of screening programs, there are still high rates of false positives and false negatives. Indeed, women were called back for additional diagnoses based on suspicious results that eventually led to cancer. Artificial intelligence (AI) algorithms represent a promising solution to improve the accuracy of digital mammography offering, on the one hand, the possibility of better cancer detection, and, on the other hand, improved efficiency for radiologists for good decision-making. In this work, through a review of the literature on the tools used to evaluate the performance of AI systems dedicated to early detection and diagnosis of breast cancer. We set out to answer the following questions: Is the ethics relating to patient data during the development phase of this software is respected? Do these tools take into consideration the specificities of the field? What about the specification, accuracy and limitations of these applications? At the end, we show through this work recommendations to adapt these evaluation tools of AI applications for breast cancer screening for an optimized and rational consideration of the principle of health vigilance and compliance with the regulatory standards in force governing this field.

We identify obstructive sleep apnea as the most common respiratory issue associated with sleep. Frequent breathing disruptions characterize sleep apnea during sleep due to an obstruction in the upper airway. This illness, if left untreated, can lead to significant health problems. This article outlines a sound approach for detecting sleep apnea and tracking it in an automated and intelligent manner. The method entails an automated identification of OSA based on the sound signal during breathing and a cardio-respiratory signals analysis for more efficient results. The suggested approach is put to the test under a variety of scenarios to verify its efficacy and dependability. The benefits and drawbacks of the suggested algorithm are mentioned further down.