Examples from Industry, Government, and Academia
One of the areas enjoying some of the highest degree of AI innovation is health, taken here to mean both life sciences and healthcare delivery. Health is a huge market and is attractive to AI researchers given the tremendous volume and variety of health data being produced constantly, as well as the potential for improving care. At the same time, the health industry possesses unique challenges to overcome before meaningful progress can be achieved.
The scale of data surrounding people’s health is constantly increasing, both in depth and in breadth. AI algorithms today can leverage a wide variety of sources, including:
This multidimensional slew of data, coupled with modern developments in statistical theory, programming, and computational hardware, means that it is a truly exciting time to be applying AI to questions of health.
But while there are many exciting developments underway and on the horizon for AI in health, it is important to realize that AI is not a panacea. It is important to realize that there are some very real challenges to accessing the data required for, or developing predictive models about, biomedical problems.
1The Cost of Sequencing a Human Genome. National Human Genome Research Institute. https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost (2019).
Mathias Bellaiche, PhD
Senior Consultant, Advanced Analytics and Intelligent Automation
(479) 409-5745
mabellaiche@guidehouse.com
Acknowledgements
The author would like to acknowledge the following people for their contributions: Sri Iyer, Bassel Haidar, Alex Gromadzki, Kate Pokrass, Samuel Soltoff, and Winnie Fan.