By some estimates, the U.S. healthcare system now generates over one trillion gigabytes of data annually. And with this proliferation of information, inexpensive, large-scale computing power has found itself meeting this data at the edge of many healthcare enterprise networks.

This newly developed collaboration, if you will, is now creating an artificial intelligence (AI) and machine learning (ML) tidal wave in the health and healthcare industries. The plan for this collaboration is for them to uncover and deliver the insights required to accelerate the discovery of new therapeutics, while making the effectiveness and delivery of existing therapeutics even more effective.

Artificial intelligence and machine learning are now being utilized in more than therapeutic development and healthcare delivery in a variety of ways. Pharma and medical-technology manufacturers are using machine learning to drive development, guide clinical-trial design, and determine phases of life-cycle management. This is an important area in which AI and ML can make a compelling contribution, as they can aid in monitoring and compiling information that will compress the timelines in understanding variations in responses to treatment. Additional uses of machine learning include informing diagnosis, developing treatment algorithms, and employing new digitally-based therapeutics.

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