7 factors that will push implementation of AI in healthcare

Additionally, Naylor explained that while AI and deep learning serve as an analytic and modeling tool, they also represent the current convergence of health and data sciences. "Barriers to adoption will rightly be more rigid in healthcare than in many other fields in which software programs relying on deep learning and other forms of machine learning are used daily by billions of people," Naylor wrote. "However, pressure to deploy deep learning and a range of tools derived from modern data science will be relentless, given the extraordinarily rich information now available to characterize and follow vast numbers of patients, the ongoing challenges of making sense of the complexity of human biology and healthcare systems, and the potential for smart information technology to support tomorrow's clinicians in the provision of safe, effective, efficient and humanistic care." In a partnering viewpoint, Geoffrey Hinton, PhD, a cognitive psychologist and computer scientist with Google's Brain Team and University of Toronto, explained that deep learning has undoubtedly changed the healthcare landscape, especially in image interpretation, speech recognition and language translation. Because artificial neural networks of deep learning mirror the brain's ability to learn difficult patterns, Hinton noted that the networks also model complicated between inputs and outputs used for predicting future medical events from past events or large data sets.