Artificial intelligence and machine learning can be used to advance healthcare and accelerate life sciences research. And there are many companies on the market today with AI offerings to do just that. Derek Baird is president of North America at Sensyne Health, which offers AI-based remote patient monitoring for healthcare provider organizations and helps life sciences companies develop new medicines. Baird believes some large companies have missed the mark on AI and ultimately dismantled public trust in these types of technologies, but that some companies have cracked the code by starting with the basics. He also believes AI success hinges on solving non-glamorous issues like data normalization, interoperability, clinical workflow integration and change management.

There is no universally agreed definition of AI. Broadly speaking, AI tends to refer to computing technologies that replicate or resemble processes and tasks associated with human intelligence, such as reasoning, sensory understanding, and interaction. AI technologies work in different ways, but most use large quantities of data to produce an output. For example, machine learning, a type of AI that has been particularly successful in recent years, works by learning and deriving its own rules from data and experience. At the moment, most health-related applications of AI are at the research or early trial stage and it is not yet clear how successful they will be in wider healthcare systems.