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.
Artificial intelligence just might be the next major technologic breakthrough to affect health care delivery, with seemingly endless possibilities for the improvement of patient care and optimization of the health care system overall.1 Simply defined, artificial intelligence is a field of computer science focused on enabling computers to complete tasks or generate knowledge that, in the traditional sense, would typically require human intelligence. Within the topic of artificial intelligence, there are the fields of machine learning and deep learning.
In medicine, we often say that when you hear hoof beats, think horses, not zebras. The idea, of course, is that common diseases are common. Focusing on common diseases addresses those diseases that affect the most people. That's been a good plan in initial attempts to bring deep-learning systems to medicine. The NIH Chest Radiograph CXR14 data set, for example, focused on 14 common imaging findings.
"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.