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From diagnosis and pathology to drug discovery and epidemiology, healthcare’s reliance on large amounts of data makes it one of the most exciting frontiers of artificial intelligence
Slowly but surely, artificial intelligence is infiltrating almost every aspect of our lives. It is already busy in the background of many routine tasks, powering virtual assistants like Siri and Alexa, recommendations from Amazon and Netflix, and underpinning billions of Google searches each day. But as the technology matures, AI’s impact will become more profound, and nowhere is that more apparent than in healthcare.
Healthcare’s data-heavy nature makes it an ideal candidate for the application of AI across multiple disciplines, from diagnosis and pathology to drug discovery and epidemiology. At the same time, the sensitivity of medical data raises fundamental questions around privacy and security. This juxtaposition makes healthcare one of AI’s most exciting frontiers and also potentially one of its most dangerous.
One of the biggest — and most lucrative — applications of artificial intelligence (AI) is in health care. And the capacity of AI to diagnose or predict disease risk is developing rapidly. In recent weeks, researchers have unveiled AI models that scan retinal images to predict eye- and cardiovascular-disease risk, and that analyse mammograms to detect breast cancer. Some AI tools have already found their way into clinical practice.
AI diagnostics have the potential to improve the delivery and effectiveness of health care. Many are a triumph for science, representing years of improvements in computing power and the neural networks that underlie deep learning. In this form of AI, computers process hundreds of thousands of labelled disease images, until they can classify the images unaided. In reports, researchers conclude that an algorithm is successful if it can identify a particular condition from such images as effectively as can pathologists and radiologists.