Personalizing Healthcare with Data & AI

That AI is one of the biggest breakthrough in medicine is not much of an exaggeration. Here we describe a few trends and speculate on a future path of AI in the healthcare sector. Since AI depends on data, growth in wearable devices or “medical IoT”<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Sometimes “wellness IoT” or “monitoring IoT” have been fueling AI and its applications to medicine and wellness. With incessant growth comes new taxonomy<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>Terms used in this paper are yet to be standardized; we merely attempt to convey an idea as clearly as we can.; little has been standardized and we use terms which are likely to be changed as the industry matures. Its proliferation brings a plethora of risks associated with data acquired by IoT. Those risks include things like data breaches, denial of service (DoS) attacks, configurations<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>Risk of hackers messing with configurations., ransomware, and other risks which are yet to be realized and addressed. We offer plausible approaches how security may be addressed.