Several AI methods can be applied in the healthcare field. A simple but effective area where AI has already been used for some time is the image recognition on radiological and ophthalmological problems. In some cases, AI software outperforms the experts in disease diagnosis, such as breast and lung cancer detection. Retinography tasks, such as the understanding of retina images or the detection of blind spot injuries due to diabetes, have also been widely studied. For instance, some publicly available software has been built to make diagnoses or even infer diseases such as macular degeneration compared to human performance for some datasets. The most obvious use of AI software for this is to provide faster and more efficient screening systems, as the proposed software provides faster solutions with great benefits associated with this. The use of artificial intelligence in healthcare has been rapidly growing and is gaining more importance progressively. AI has opened novel approaches in the area and enabled us to achieve solutions that were impossible to think about or implement before. The rapid growth of data in the healthcare industry allows researchers and healthcare systems to explore and build innovative methods that could be used in diagnosis, early disease detection, prognosis systems, and many other healthcare tasks. The rapid growth of healthcare data has promoted an opportunity to gain new insights, develop new tools and software, and extract new patterns to build more accurate models and methods that ultimately turn the healthcare systems smarter.