The rise of additive manufacturing, also known as 3D printing, has transformed numerous industries across the globe. Healthcare in particular has the potential for disruption, as 3D printing has, and will continue to, provide new areas of cost savings, efficiency, and convenience across the medical industry.[1]
Microsoft is entering the exam room to help reduce physician burnout and improve patient outcomes through voice recognition and machine learning. Can it work?
Medical device manufacturers such as Medtronic are uniquely positioned to benefit from advances in additive manufacturing – both for prototyping as well as the product attributes possible with this technique. Medtronic must continue to invest aggressively in this technology in order to stay ahead of competition.
Creating smart hospitals using artificial intelligence
Machine Learning (ML) is changing the way healthcare systems operate, both in terms of internal processes and patient-facing interventions. How effective will it prove in the long run and will it be a tool to aid or a tool to replace traditional operations – these are the questions that the healthcare industry faces today.
Exploring the Department of Defense's bio-additive manufacturing gambit to address mounting soldier care shortcomings
Machine learning is disrupting traditional industries such as transportation (ride-hailing) and financial services (credit scoring, robo-advisory). Can it also transform the traditionally human-intensive healthcare industry too?
In the realm of deep learning, your eyes can be a window to your health. Google aims to leverage machine learning for high tech healthcare solutions.
Explore the crafting process of customized titanium bone for orthopedic surgeries & its potential impact to the overall healthcare sector.
Machine learning has been touted as a potential cure-all for high drug prices. A number of leading biopharmaceutical companies like Roche have made some large bets in using artificial intelligence tools to improve drug development, but it remains to be seen if machine learning is living up to its hype.
