ML for Life Sciences: The Next Technological Boom
FREMONT, CA: The diverse integration of AI technology is still to be realized, in the sphere of life sciences. Machine learning in the setting of life sciences can be used to identify disease phenotypes quickly and precisely, learn and anticipate from structured biological data and image-based data, and enhance patient safety and development of drugs. With massive hardware and Big Data enhancements, machines can sense, understand, interact, anticipate, and react positively to business issues in the industry. Bio-pharmaceutical brands are vital to life science organizations ' intellectual property, and marketing intelligence and insights are surprising approaches to improving brand recognition and marketing ROI.
Life science organizations are spending huge sums with contract companies on direct and indirect materials and services. ML services help supervisors to optimize spending around the world. ML uses cases in critical procurement and acquisition include analysis of contract-negotiation behavior, optimization of contract grants to suitable candidates, identification of single source threats, and assurance of outsourcing segments to contract manufacturers. Sales and marketing can use ML through sales dealings with wholesalers, medical clinics, health facilities, and retail drug stores by grabbing keywords and new contacts to feed into deal scoring, ultimately improving the rate of success.
Check out: Top Artificial Intelligence Companies
Companies in life sciences can use AI to boost sales as well as secure the brand by understanding which distributors or patients are least prone to change to a generic based on previous patterns so that they can concentrate their efforts on other people who need to be all the more inductive. Machine learning undoubtedly has enormous significance for businesses in the life sciences. The key is to ensure that the components are set up to use most of that data in their models and the human capacity to understand which discoveries need consideration and can be largely ignored. We just touched the superficial layer of what machine learning can accomplish for life science businesses, though. Seeing where it's going next is going to be overwhelming.
By Leni Kaufman, VP & CIO, Newport News Shipbuilding
By George Evans, CIO, Singing River Health System
By John Kamin, EVP and CIO, Old National Bancorp
By Elliot Garbus, VP-IoT Solutions Group & GM-Automotive...
By Gregory Morrison, SVP & CIO, Cox Enterprises
By Alberto Ruocco, CIO, American Electric Power
By Sam Lamonica, CIO & VP Information Systems, Rosendin...
By Sergey Cherkasov, CIO, PhosAgro
By Pascal Becotte, MD-Global Supply Chain Practice for the...
By Stephen Caulfield, Executive Director, Global Field...
By Shamim Mohammad, SVP & CIO, CarMax
By Ronald Seymore, Managing Director, Enterprise Performance...
By Brad Bodell, SVP and CIO, CNO Financial Group, Inc.
By Jim Whitehurst, CEO, Red Hat
By Clark Golestani, EVP and CIO, Merck
By Scott Craig, Vice President of Product Marketing, Lexmark...
By Dave Kipe, SVP, Global Operations, Scholastic Inc.
By Meerah Rajavel, CIO, Forcepoint
By Amit Bahree, Executive, Global Technology and Innovation,...
By Greg Tacchetti, CIO, State Auto Insurance