While a robot doctor at the bedside is not on the horizon, data-driven digital health is transforming how we receive care - and society is still playing catch-up on the ramifications. In 2012, Professor Enrico Coiera, Founding Director of the Centre for Health Informatics (CHI) at the Australian Institute for Health Innovation, published a paper titled The Dangerous Decade. In it, he warned that more information and communication technology (ICT) would be deployed into healthcare in the 10 years to 2022 than in the health system's entire history to date. "Systems will be larger in scope, more complex, and move from regional to national and supranational scale," he wrote. "Yet we are at roughly the same place the aviation industry was in the 1950s with respect to system safety."

Machines are taking over, even venturing into the dark arts of beer brewing in an artificial intelligence experiment at Lot Fourteen.

Zygmunt received his PhD degree in Computer Science from the University of Adelaide, Australia in 2013, and his MSc degree in Computer Science from the University of KwaZulu-Natal, South Africa in 2009. He is a senior research fellow at the Australian Institute for Machine Learning. His research lies at the interface of computer vision, machine learning, and challenging industry problems. He develops algorithms that allow computers to perform tasks typically associated with human intelligence. In the last couple of years, his work has focused on the application of machine learning and image processing techniques for the development of smart medical devices.

If a picture tells a thousand words, these are the two jostling foremost in a patient's mind when a radiologist scans their body for a better image of that suspicious lump or mass. But there is so much more a picture can tell us about cancer, particularly if we consider the possibilities of artificial intelligence. In 2017, US scientists announced they had developed an algorithm, or a computerised tool, to identify skin cancers through analysis of photographs. The algorithm scans a photo of a patch of skin to look for common forms of skin cancer, performing on par with board-certified dermatologists in identifying malignant melanomas (the third most common cancer in Australia) and keratinocyte carcinoma. This technology might enable skin cancer detection in country clinics and suburban GPs' offices at the highest accuracy available.

Technology and innovation company Lockheed Martin Australia has become the first Foundation Partner with the University of Adelaide's new Australian Institute for Machine Learning. The strategic partnership will deliver world-leading machine learning research for national security, the space industry, business, and the broader community. Machine learning is a form of artificial intelligence that enables computers and machines to learn how to do complex tasks without being programmed by humans. This technology is driving what is known as the "fourth industrial revolution". The University's new Australian Institute for Machine Learning (AIML) – which builds on decades of expertise in artificial intelligence and computer vision – will be based in the South Australian Government's new innovation precinct at Lot Fourteen (the old Royal Adelaide Hospital site).