The world's leading drug companies were turning to artificial intelligence (AI) to improve the hit-and-miss business of finding new medicines, with GlaxoSmithKline (GSK) unveiling a new $43-million (R562-million) deal in the field on Sunday. Other pharmaceutical giants including Merck & Co, Johnson & Johnson and Sanofi were also exploring the potential of AI to help streamline the drug discovery process. The aim was to harness modern supercomputers and machine learning systems to predict how molecules would behave and how likely they were to make a useful drug, thereby saving time and money on unnecessary tests. AI systems already played a central role in other high-tech areas such as the development of driverless cars and facial recognition software.
"A cognitive business takes advantage of recent developments in cognitive computing to improve the overall effectiveness of its people, processes and technology. Data is starting to be pulled from more and more sources today to help solve problems in diverse fields – from health care to national defense and from daily operations to setting the right metrics that measure progress towards strategic and tactical goals. In 2016, the amount of global data being collected and analyzed is unprecedented--and growing. Working with that data in smarter ways is the key to future business success. For example, IBM's Watson relies on deep learning algorithms and neural networks to process information by comparing it to a teaching set of data in research hospitals to diagnose symptoms and recommend better patient treatment plans.
Artificial intelligence (AI) and machine learning are driving a great deal of the healthcare innovation in precision medicine, according to a new Chilmark Research report. The report reveals achieving the full potential of precision medicine is impossible to realize without applying AI and machine learning. Specifically, leveraging advanced machine learning and deep learning technology can rapidly analyze large datasets that outperform clinicians and researchers. The concept of precision medicine is starting to become a reality due to new medical data from the All of Us research program, CAR-T therapies, increasingly accessible genetic testing, and other apps. As these new data-driven, personalized treatment plans begin to enter clinical practice in specialty care settings such as oncology and mental health, it is now time to assess the limits of current health IT ecosystems to broader clinical adoption, and where the opportunities lie for innovative solutions to bring precision medicine into the mainstream.