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Long Reads


Calling Dr. Robot

As in many other economic sectors, artificial-intelligence applications in medicine seem to hold unlimited promise. But to realize AI’s full potential in diagnosis, records management, hospital operations, and other areas of medicine, innovators and regulators alike must heed the lessons of past technological revolutions that failed.

PARIS – Unintended consequences in the field of artificial intelligence (AI) tend to make for lively headlines, such as when Microsoft introduced a Twitter chatbot that quickly began spewing racist slurs. But whether it is a case of Google’s image-recognition algorithm labeling black people as “gorillas” or Tesla’s autonomous vehicles killing their drivers, AI’s controversies have yet to dampen its appeal.

As AI applications multiply, so, too, will the reported failures, leading eventually to a public and regulatory backlash. Nowhere is this truer than in health care, where investment in AI reached an all-time high in the second quarter of 2018. From alleged medical-device failures in Canada and Europe to recent concerns about the performance of IBM’s Watson Health, the risks of adopting new technologies in the health-care sector are clear.

But AI also promises to revolutionize the management of health records and patient risk, diagnosis, hospital operations, and other areas of medicine. It is little wonder, then, that the global AI health-care market is expected to surpass $34 billion by 2025. And public support for the use of AI in health care is already high across a broad range of countries. In the United States, for example, 53,000 patient-monitoring devices – each gathering data for AI-driven predictive analytics – were in use by the end of 2017. That number is expected to reach 3.1 million by 2021 – an annual growth rate of 176%.

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