Özet:

The goal of this study is to help create a perspective about usage of Artificial Intelligence in some branches of medicine as oncology, radiology, surgery, emergency medicine, etc. A literature review of “Artificial Intelligence in medicine” and for the use of artificial intelligence in some medical branches has been done. Radiology is one of the most notable in the artificial intelligence field and open to many developments in this field. Ten millions of radiology reports and billions of images are now digitally accumulated, simplifying the “big data” concept and creating the bottom line for Artificial Intelligence research. Pathologists used Artificial Intelligence to reduce the error rate of diagnosis of cancer-positive lymph nodes. The accuracy of cancer prediction results has increased considerably in 15-20% of recent years with the application of Machine Learning techniques. Two deep learning systems trained to detect and treat diabetic retinopathy and macular edema achieved high specificities (98%) and sensitivity (87% - 90%) to detect moderate retinopathy and macular edema using a large of retinal photographs in ophthalmology. Several Machine Learning models have promised to develop current triage methods in the Emergency Departments. Surgeons will likely see the Artificial Intelligence analysis of the population and patient-specific data in the future. Artificial Intelligence can certainly help doctors make better clinical decisions and judgments in certain functional areas in health care.

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