While the use of machine learning and artificial intelligence in medicine has its roots in the earliest days of the field, it is only in recent years that there has been a push towards the recognition of the need to have healthcare solutions powered by machine learning.

This has led researchers to suggest that it is only a matter of time before machine learning to be ubiquitous in healthcare. Despite the recognition of the value of machine learning (ML) in healthcare, impediments to further adoption remain. One pivotal impediment relates to the black box nature,  or opacity, of many machine learning algorithms. This has led to the exploration of Explainable models in healthcare.

In this tutorial, you will:

• Learn the impact of explainable ML models in healthcare and how they are making an impact
• See examples of explainable models being used in real life scenarios
• Understand how a partnership with KenSci can help you make your ML models more accountable

Submit the form to get started!