No abstract

The intersection of these two trends is what we call The Issue and it is helping businesses in every industry to become more efficient and productive. One's aim is to have an insight into the development and maintenance of comprehensive and integrated health information systems that enable sound policy and effective health system management in order to improve health and health care. Undeniably, different sorts of technologies have been developed, each with their own advantages and disadvantages, which will be sorted out by attending at the impact that Artificial Intelligence and Decision Support Systems have to everyone in the healthcare sector engaged to quality-of-care, i.e., making sure that doctors, nurses, and staff have the training and tools they need to do their jobs.

Lipoma arborescens is a benign tumor, but it may be a reactive process to other disorders, and its clinical, analytical, radiological and ultrasound presentation may be redundant to any synovial tumor. Despite the characteristic feature on magnetic resonance imaging (MRI), the correct differential diagnosis in atypical presentation, and the need for timely removal of the lesion to prevent joint damage, forces, ultimately, to invasive procedures. The clinical case reported here, fourth described in English language publications on the polyarticular form, also presented other specificities related to one of the swellings, in the knee. Because of its atypical location in the popliteal fossa, recurrent episodes of joint effusion, personal history of knee trauma, pulmonary tuberculosis, and family history of rheumatoid arthritis required particular attention. This process was hampered by the refusal of knee (and ankle) surgery by the patient. He accepted surgical removal of the swellings of the wrists, for aesthetical reasons, with pathologic confirmation of the diagnosis, and clinical success in that location. MRI of the knee showed the typical image of lipoma arborescens, but also other changes that compromise the prognosis.

Deep Learning (DL) is a new area of Machine Learning research introduced with the objective of moving Machine Learning closer to one of its original goals, i.e., Artificial Intelligence (AI). DL breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. Better preventive healthcare, even better recommendations, are all here today or on the horizon. However, keeping up the pace of progress will require confronting currently AI's serious limitations. The last but not the least, Cervical Carcinoma is actuality a critical public health problem. Although patients have a longer survival rate due to early diagnosis and more effective treatment, this disease is still the leading cause of cancer death among women. Therefore, the main objective of this article is to present a DL approach to Case Based Reasoning in order to evaluate and diagnose Cervical Carcinoma using Magnetic Resonance Imaging. It will be grounded on a dynamic virtual world of complex and interactive entities that compete against one another in which its aptitude is judged by a single criterion, the Quality of Information they carry and the system's Degree of Confidence on such a measure, under a fixed symbolic structure.