The field of Computer Science and predictive modeling is experiencing substantial growth, with a particular focus on Artificial Intelligence (AI). Over the past decade, there has been a significant upsurge in the utilization of AI in the medical field, a trend that continues to evolve. One of the most challenging domains within medical science is oncological histopathology, which involves the diagnosis and prognosis of malignant tumors, commonly referred to as cancer. Consequently, medical professionals have turned to AI to enhance the efficiency and precision of these tasks. Specifically, two facets of AI, computer vision and deep learning, gained prominence. These technologies are now extensively employed by medical practitioners to achieve their objectives. By applying deep learning techniques to extensive datasets of cancer imaging, researchers aim to develop predictive AI models capable of determining the presence of cancerous cells in patient imaging results. As this field expands, it encounters ongoing challenges. This article seeks to elucidate the significant issues encountered within existing AI models and the data from which these models derive their knowledge. Moreover, it outlines comprehensive solutions to these challenges, with a strong emphasis on educating future generations on the methodologies for addressing these issues effectively.