**12. Conclusions**

The use of AI and computer vision algorithms, especially neural networks, has advanced greatly in recent years. The various applications with different types of medical images have made numerous diagnostic and prognostic applications available to the medical field. The field of oncology has seen the greatest number of developments. Particularly, computational pathology applied to oncology has developed a high degree of diversification in vision tasks, achieving models that could perform diagnosis, subtyping, grading, staging, and prognosis. However, just as innovative applications have emerged, the field has also had to overcome obstacles, which are still complex to analyze for some conditions today. The difficulty of constructing medical datasets, the variability of samples between different institutions, and the mandatory data protection are some of them. However, these obstacles have promoted the creation of ideas to overcome them and that is how we have neural compression and stain normalization that can be great allies to exponentially expand the datasets. Finally, the COVID-19 pandemic was a major trigger for research in AI and computer vision applied to the field of medical imaging, specifically lung imaging. It could be seen that a modeler of the research landscape was the feasibility in the clinical field. In fact, the ease of use, the short operating time, and the possibility of maintaining sterility were part of the parameters that promoted the use of ultrasonography expanding the research with deep learning in this imaging modality. Despite these great advances, more studies must be done to further refine computer vision models to ensure that patients receive the best quality of medical care.

*The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision DOI: http://dx.doi.org/10.5772/intechopen.110133*
