Machine learning in medicine has recently made headlines. Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. Stanford is using a deep learning algorithm to identify skin cancer. It is clear that machine learning puts another arrow in the quiver of clinical decision making.

Still, machine learning lends itself to some processes better than others. Algorithms can provide immediate benefit to disciplines with processes that are reproducible or standardized. Also, those with large image datasets, such as radiology, cardiology, and pathology, are strong candidates. Machine learning can be trained to look at images, identify abnormalities, and point to areas that need attention, thus improving the accuracy of all these processes.

The session aims to present how to develop a machine learning model to detect breast cancer in the early stages and covid-19 detection and how to optimize the model in order to obtain better results.

Session presenters