Clear ethical standards and guidance must be in place to protect the relationship of trust between doctors and patients and to safeguard human rights, according to a Council of Europe report today, written by Dr Brent Mittelstadt, an Oxford expert specialising in Artificial Intelligence and medical ethics. Dr Mittelstadt is Director of Research at the Oxford Internet Institute and a leading data ethicist. He says, 'I hope the report will make people think about how AI might disrupt the core practices involved in healthcare.' But he is concerned AI could be used as a way to reduce budgets or save costs rather than to improve patient care and says, 'If you're going to introduce new technology into the clinical space, you need to think about how that will be done. Too often it is seen solely as a cost-saving or efficiency exercise, and not one which can radically transform healthcare itself.'

At 2 a.m. in February, I found myself speaking with the family of a dying man. We had never met before, and I had only just learned of the patient. As an ICU doctor, I have been in this situation on many occasions, but there was something new this time. The family was 200 miles away, and we were talking through a video camera. I was staffing the electronic intensive care unit, complete with a headset, adjustable two-way video camera, and six screens of streaming data. The eICU at Emory University in Atlanta provides care by physicians trained in critical care medicine to a number of hospital locations within the large Emory system.

Artificial intelligence systems simulate human intelligence by learning, reasoning, and self correction. This technology has the potential to be more accurate than doctors at making diagnoses and performing surgical interventions, says Jörg Goldhahn, MD, MAS, deputy head of the Institute for Translational Medicine at ETH Zurich, Switzerland. It has a "near unlimited capacity" for data processing and subsequent learning, and can do this at a speed that humans cannot match. Increasing amounts of health data, from apps, personal monitoring devices, electronic medical records, and social media platforms are being brought together to give machines as much information as possible about people and their diseases. At the same time machines are "reading" and taking account of the rapidly expanding scientific literature.

Videos of increasingly agile robots from Boston Dynamics steal headlines about the AI-apocalypse, while AI's application in other industries can be all but ignored by the mainstream. However, amid all the worry about AI taking jobs, there's little informed debate and nowhere is this more true than in healthcare. As far as the public perception of AI and health, we have struggled to move beyond the idea of robot doctors. The reality of AI's likely influence in health is more nuanced and, I'd argue, more exciting. The healthcare sector is already a key battleground in the coming AI revolution, with the AI health market expected to reach $6.6 billion by 2021.

Last month, dermatologists were told they had narrowly lost a competition. "Man against machine," a study by Holger Haenssle and colleagues, found that artificial intelligence known as deep learning convolutional neural network edged out 58 dermatologists in the photographic diagnosis of melanoma. They were charged with differentiating melanoma from benign moles using images obtained via dermoscopy, a technique that allows dermatologists to view the skin through a high-quality magnifying lens with a powerful lighting system. This story made headlines: "AI beats doctors at cancer diagnoses." As dermatologists, we read the headlines with surprise and thought, "Aren't we on the same team?"

There is a school of thought that sees artificial intelligence as a direct synonym for replacement of humans by automated machines. But the reality is not quite so black and white. After all, we as humans are far more than computer code; we live and decide and even thrive on ambiguity and nuance. Nowhere is this more true than in healthcare, where analytical data can only help improve treatment when it is consolidated with social, behavioural and contextual information unique to each individual. Only with this multidisciplinary knowledge is it possible for doctors and therapists to define the best course of action for their patients.

Machines are getting better and better at analyzing complex health data to help physicians better understand their patients' future needs. In a study out today in Nature Digital Medicine, an advanced algorithm evaluated de-identified electronic health records of more than 216,000 adult patient hospitalizations to predict unexpected readmissions, long hospital stays, and in-hospital deaths more accurately than previous approaches. I caught up with one of the authors, Nigam Shah, MBBS, PhD, an associate professor at Stanford, to learn about the new study and discuss the implications for artificial intelligence in medicine. What is deep learning and how does it fit in the larger universe of artificial intelligence? Deep learning is one of several machine learning techniques that can be used to build intelligent systems.

It's been more than 20 years since Garry Kasparov lost his famous chess match against IBM's Deep Blue, which heralded much anxious commentary about how... It's been more than 20 years since Garry Kasparov lost his famous chess match against IBM's Deep Blue, which heralded much anxious commentary about how humanity was soon to be subjugated by the superior processing power of supercomputers. Two decades later and our cultural understanding of AI can feel like it's still stuck in the nineties. Videos of increasingly agile robots from Boston Dynamics steal headlines about the AI-apocalypse, while AI's application in other industries can be all but ignored by the mainstream. However, amid all the worry about AI taking jobs, there's little-informed debate and nowhere is this truer than in healthcare. As far as the public perception of AI and health, we have struggled to move beyond the idea of robot doctors.

It's been more than 20 years since Garry Kasparov lost his famous chess match against IBM's Deep Blue, which heralded much anxious commentary about how... It's been more than 20 years since Garry Kasparov lost his famous chess match against IBM's Deep Blue, which heralded much anxious commentary about how humanity was soon to be subjugated by the superior processing power of supercomputers. Two decades later and our cultural understanding of AI can feel like it's still stuck in the nineties. Videos of increasingly agile robots from Boston Dynamics steal headlines about the AI-apocalypse, while AI's application in other industries can be all but ignored by the mainstream. However, amid all the worry about AI taking jobs, there's little-informed debate and nowhere is this truer than in healthcare. As far as the public perception of AI and health, we have struggled to move beyond the idea of robot doctors.

In what ways could NLG be used in healthcare? What will NLG mean for patients? NLG can be used to empower patients, so that they understand their medical conditions and can make better choices about their healthcare. NLG can also help patients do a better job of looking after themselves: this includes lifestyle changes, self-management of chronic conditions, and complying with treatment regimes. For example, many diabetics have sensors which measure blood sugar levels, but they struggle to use this information to manage their diabetes because often they don't understand it, and can overreact and indeed panic when they see their blood sugar change.