By analyzing CT scans from 48 patients, the deep learning algorithms could predict whether they'd die within five years with 69 percent accuracy -- "broadly similar" to scores from human diagnosticians, the paper says. "Instead of focusing on diagnosing diseases, the automated systems can predict medical outcomes in a way that doctors are not trained to do, by incorporating large volumes of data and detecting subtle patterns." For this study, the system was looking for things like emphysema, an enlarged heart and vascular conditions like blood clotting.The deep learning system was trained to analyze over 16,000 image features that could indicate signs of disease in those organs. The goal was not to build a grim diagnostic system, and the AI only analyzed retrospective patient data.
"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.
Inferences were made using traditional biostatistics. In the early 1990s, ML emerged, whereby advanced computing programs (machines) processed huge data sets (big data) from many sources and discerned patterns among multiple unselected variables. Such patterns were undiscoverable using traditional biostatistics (1) and were used to iteratively refine (learn) layered mathematical models (algorithms). The Table lists key differences between EBM and ML.
AlphaGo's uncanny success at the game of Go was taken by many as a death knell for the dominance of the human intellect, but Google researcher David Silver doesn't see it that way. Instead, he sees a world of potential benefits. As one of the lead architects behind Google DeepMind's AlphaGo system, which defeated South Korean Go champion Lee Se-dol 4 games to 1 in March, Silver believes the technology's next role should be to help advance human health. "We'd like to use these technologies to have a positive impact in the real world," he told an audience of AI researchers Tuesday at the International Joint Conference on Artificial Intelligence in New York. With more possible board combinations than there are atoms in the universe, Go has long been considered the ultimate challenge for AI researchers.