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.

Health care leaders are embracing AI. But by conducting an extensive review of case studies and research literature, we've found that their AI initiatives are predominantly focused on developing algorithms that can predict a problem such as cancer in order to make diagnoses better, faster, and less expensively. Rarely, are their organizations devoting resources to AI efforts aimed at understanding why diseases occur. To intervene as effectively as possible, both kinds of algorithms are crucial. To be clear, we are not downplaying the importance of predictive analytics to help diagnose patients.

Health care leaders are embracing AI. But by conducting an extensive review of case studies and research literature, we've found that their AI initiatives are predominantly focused on developing algorithms that can predict a problem such as cancer in order to make diagnoses better, faster, and less expensively. Rarely, are their organizations devoting resources to AI efforts aimed at understanding why diseases occur. To intervene as effectively as possible, both kinds of algorithms are crucial. To be clear, we are not downplaying the importance of predictive analytics to help diagnose patients.

The Healthy Nevada Project, developed by Renown Institute for Health Innovation (Renown IHI), is one of the first community-based population health studies in the United States. By combining genetic data, environmental data and individual health information, researchers and physicians are gaining new insight into population health, enabling personalized health care while improving the health and well-being of entire communities in Nevada. The Project comes at a time when the state continues to struggle with poor health outcomes and excess costs. Nevada ranks near the bottom of overall health rankings in the U.S. and suffers from high mortality rates for chronic conditions like heart disease, cancer and chronic respiratory disease. "This was our call to action," says Dr. Anthony Slonim, President and CEO of Renown Health.

Agencies are pushing ahead on various IT modernization efforts all the time. But how do they actually make those initiatives successful, and what are the technologies that will have the most impact? Agencies that have embraced technology change are already starting to see the benefit of disruptive models to deliver their missions and better outcomes in new ways, according to Accenture Federal Services' "Federal Vision 2030" report. "And they are creating more value for citizens and empowering employees with exciting new ways to serve," the report says. "Other less agile agencies fall behind in applying the latest technologies and approaches to reimagine the mission and business. When this gap widens, public trust declines and workforce engagement drops -- and external adversaries may stoke these instabilities."

The worst foreign-policy decision by the United States of the last generation – and perhaps longer – was the "war of choice" that it launched in Iraq in 2003 for the stated purpose of eliminating weapons of mass destruction that did not, in fact, exist. Understanding the illogic behind that disastrous decision has never been more relevant, because it is being used to justify a similarly misguided US policy today. The decision to invade Iraq followed the illogic of then-US vice-president Richard Cheney, who declared that even if the risk of WMD falling into terrorist hands was tiny – say, 1% – we should act as if that scenario would certainly occur. Such reasoning is guaranteed to lead to wrong decisions more often than not. Yet the US and some of its allies are now using the Cheney Doctrine to attack Chinese technology.

I hate it when I hear senior executives state that they want to become data-driven, as if somehow having data is value in of itself. Now, one can hardly blame the unenlightened executive whose only perspectives on data are associated with statements like "Data is really the new oil" (Wall Street Journal) or "The world's most valuable resource is no longer oil, but data" (The Economist). The infatuation with "data-driven" versus "value-driven" can be confirmed from Google Trends (Figure 1). However, this is where the value determination of data and oil diverge. Oil has value as determined by Generally Accepted Accounting Principles (GAAP).

Some say allowing artificial intelligence (AI) to determine guilt or innocence in a courtroom is a step too far. But for those who are sceptical about the neutrality of human judgment, or have witnessed an unfair justice system in action, AI and legal robots could be the answer to providing a fair and impartial jury. We already automate so much else in society, so why not extend this smart automation to juries? After all, lawyers rely on technology to scan documents for keywords or evaluate collected data. And people can now use legal chatbots to determine if it's worthwhile to pursue their case in court.

Do you think the traffic is bad where you live? Try moving to Boston, where commuters suffer the worst highway congestion in the nation. Residents of the New England city spent an average of 164 hours sitting in their vehicles going nowhere slowly last year, losing as much as $2,291 in personal value for the privilege. And that's nothing compared to the city found to be cursed with the worst highway tie-ups on the planet. Moscow commuters are known to have lost an average of 210 hours each last year to traffic jams.

ETH researchers use artificial intelligence to improve quality of images recorded by a relatively new biomedical imaging method. This paves the way towards more accurate diagnosis and cost-effective devices. Scientists at ETH Zurich and the University of Zurich have used machine learning methods to improve optoacoustic imaging. This relatively young medical imaging technique can be used for applications such as visualizing blood vessels, studying brain activity, characterizing skin lesions and diagnosing breast cancer. However, quality of the rendered images is very dependent on the number and distribution of sensors used by the device: the more of them, the better the image quality.