It's not enough for a healthcare artificial intelligence (AI) algorithm to be highly accurate. To be widely adopted in clinical use, it must demonstrate improvement in quality of care and patient outcomes, according to an opinion article published online October 29 in BMC Medicine. A team from Google Health in London, U.K., led by Dr. Christopher Kelly, PhD, said that further work is needed to develop tools to address bias and unfairness in algorithms, reduce the brittleness of AI and improve the generalizability of models, and develop methods for improving the interpretability of machine-learning predictions. "If these goals can be achieved, the benefits for patients are likely to be transformational," the group wrote. AI faces a number of challenges standing in the way of translation into clinical practice, including those intrinsic to the science of machine learning, logistical difficulties in implementation, and barriers to adoption, as well as sociocultural or pathway changes associated with using the technology, according to the team.