In a previous video we introduced the concept of machine learning aka ML. In this video we’ll discuss the way to apply ML in healthcare with some examples. Here’s what you need to know in a nutshell!
Hosts: Em Tombash, MD
Machine learning in healthcare allows us to predict a future where data, analysis, and innovation work together to help countless patients without them ever realizing it.
One of the biggest uses of machine learning in healthcare is the identification and diagnosis of diseases which are otherwise considered hard-to-diagnose. This can include anything from cancers which are tough to catch during the initial stages, to other genetic diseases. IBM Watson Genomics is a good example for showing us how integrating cognitive computing with genome-based tumor sequencing can help in making a fast diagnosis. ** https://www.ibm.com/watson-health
There is a technology called Computer Vision which both machine learning and deep learning are responsible for. This has found acceptance in the InnerEye initiative developed by Microsoft which works on image diagnostic tools for image analysis. So, in the future… Do you think machine learning and AI can eliminate radiologists? ** https://www.microsoft.com/en-us/research/project/medical-image-analysis/
Another primary clinical application of machine learning lies in the early-stage drug discovery process. Project Hanover developed by Microsoft and it uses ML-based technologies for multiple initiatives including developing AI-based technology for cancer treatment and personalizing drug combinations for AML (Acute Myeloid Leukemia). ** https://www.microsoft.com/en-us/research/project/project-hanover/
We mentioned personalized medicine and its importance for better patient care in our previous videos. We need to remember that personalized treatments are not only more effective by pairing individual health with predictive analytics but are also helpful for further research and better disease assessment. One of the leaders, IBM Watson Oncology is at the frontline of this movement by leveraging patient medical history to help generate multiple treatment options. ** https://www.ibm.com/downloads/cas/NPDPLDEZ
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