Diabetes is one of the fastest growing disorders affecting population across the globe, where diagnosis remains as big a challenge as treatment. Will Machine Learning enable healthcare companies to develop an effective disease management solution?
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.
The introduction of artificial intelligence to healthcare has sent shockwaves through the system with the promise of dramatic increases in quality of care and organizational efficiencies coupled with much needed cost reductions. The promise of AI in healthcare predicts a futuristic world where machines are primary and human doctors are secondary. The reality is tempered by the many challenges facing the widespread adoption of AI in US hospitals. A review of the adoption of IBM Watson Health at Memorial Sloan-Kettering Cancer Center provides a case study.
With their machine learning-enabled OncoEMR platform, Flatiron is giving oncologists the power of big data at the point of patient care.
What if you could test for cancer before a tumor even develops?
Will doctors— one of the most highly trained professionals— become obsolete?
Digital transformation creates huge opportunities for MSKCC and other hospitals to improve their quality of care and bottom line.
How the drug discovery process is keeping up with technology that outpaces Moore’s Law