AI can be found throughout clinics and hospitals working alongside doctors, nurses, and specialists. Automated systems help flag risk factors, identify likely conditions, and assist medical professionals with other vital forms of diagnoses and treatment. Here are some common questions I get about AI in the medical field. Machine learning and deep learning can help on multiple fronts. If we talk about computer vision, a type of AI that processes images, there are already many applications. In medical departments such as radiology, ophthalmology, and neurology, doctors perform diagnoses based on MRI scans, CT scans, X-Rays, photographic images of tissue samples, and sometimes drawings of patients.

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Artificial intelligence has risen to prominence this year. In March 2016, Google DeepMind's AlphaGo won 4-1 in the Chinese game of Go, beating the top Go player in the world over the past decade, Lee Sedol. And IBM's Watson correctly diagnosed a Japanese leukaemia patient in ten minutes after doctors had failed to diagnose her condition over several months. Navigating this information requires vastly more sophisticated tools than hitherto available. Large industry players are aware of this and Google, IBM and Microsoft are all investing heavily.

Marc Vollenweider is the CEO and co-founder of Evalueserve. As a former partner at McKinsey & Company in Zurich and India, Marc developed a deep interest in the world of data analytics, particularly how human minds and smart machines can successfully complement each other. He saw the potential for companies to enhance productivity, shorten their time-to-market, improve quality, and gain new capabilities by leveraging mind machine. Marc's fascination with innovation has led to the establishment of a series of internal ventures at Evalueserve. He is the co-inventor of the digital model of InsightBee, Evalueserve's new pay-as-you-go research and analytics solution.

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