Artificial intelligence (AI) systems simulate human intelligence by learning, reasoning, and self correction. Already this technology shows the potential to be more accurate than physicians at making diagnoses in specialties such as radiology, dermatology, and intensive care; at generating prognostic models; and at performing surgical interventions.1 And in 2017 a robot passed China’s national medical exam, exceeding the minimum required by 96 points.2

More precise, reliable, and comprehensive

Even if machines are not yet universally better than doctors, the challenge to make them better is technical rather than fundamental because of the near unlimited capacity for data processing and subsequent learning and self correction. This “deep learning” is part of “machine learning,” where systems learn constantly without the potential cultural and institutional difficulties intrinsic to human learning, such as schools of thought or cultural preferences. These systems continually integrate new knowledge and perfect themselves with speed that humans cannot match. Even complex clinical reasoning can be simulated, including ethical and economic concerns.

Increasing amounts of more comprehensive health data from apps, personal monitoring devices, electronic medical records, and social media platforms are being integrated into harmonised systems such as the Swiss Personalised Health Network.3 The aim is to give machines as complete a picture as possible of people’s health over their life and maximum knowledge about their disease.

The notion that today’s physicians could approximate this knowledge by keeping abreast …