This blog post was written by Dr. Maryam S. Jaffer, Director Data and Statistics, Emirates Health Services; Dr. Bashar Balish, Senior Director, Cerner; and Michel Ghorayeb, UAE Managing Director, SAS. The future of health care has never been more exciting. Artificial intelligence (AI) and data analytics have captured center stage for any business planning on surviving and thriving. Given the pace of technological development, AI is transforming the future on an unprecedented scale. And that includes the future of health care.

The Tesla Model S Last week it emerged that a self-driving Tesla car had been involved in a fatal crash in Williston, Florida on May 7. The car was in Autopilot mode when its on-board system failed to recognize a left-turning truck intercepting its path, resulting in the car proceeding underneath the trailer and losing its roof. There are already numerous theories about what happened, the most likely being that the car's cameras could not decipher the pale trailer against the bright Florida sky. Another possibility is that the system registered the trailer as an overhead road sign – Tesla cars are programmed to ignore overhead signs to avoid excessive and unnecessary braking – which could have happened due to the onward view beneath the trailer being clear to cameras. Nonetheless, the crash resulted in the death of 40-year-old Joshua Brown from Ohio and official investigations have begun. Since the incident, Tesla has released a blog post expressing their condolences and outlining the safety procedures all Tesla cars are equipped with.

We introduce EHS*, a novel temporal-epistemic logic defined on temporal intervals characterised by regular expressions. We investigate the complexity of verifying multi-agent systems against EHS* specifications for a number of fragments of EHS* with results ranging from PSPACE-completeness to non-elementary time. The findings show that, at least for the fragments under analysis, the increase in expressiveness obtained by using regular expressions rather than end-points as standard, can be achieved without increasing the complexity of the problem. We show that the expressiveness of regular expressions can also be adopted at the level of specifications without severe computational cost. To do so we introduce a further temporal-epistemic logic, called EHSre, in which regular expressions are used within propositions, and give a polynomial time reduction of the model checking problem from EHSre to EHS*.