There have already been some powerful indicators of Artificial Intelligence’s influence to help monitor and predict health epidemics around the world, and in one case a computer algorithm identified an Ebola outbreak nine days before the World Health Organization reported it. The computer sifted through social media sites, news reports and government websites to identify there was an outbreak. As with any algorithm, the more data it is given, the more learning is achieved and therefore the better it is in the future. Although the current work to identify outbreaks is imperfect, it has significant potential.

The Artificial Intelligence and Medical Epidemiology platform (otherwise known as AIME Inc) aims to aid in the prevention of diseases by using artificial intelligence and machine learning to predict the outbreak of epidemics. Set up in Malaysia by the epidemiologist Dr. Dhesi Raja in collaboration with two computer scientists, Dr. Peter Ho and Dr. Choo-Yee Ting, their algorithm analyses a large set of data from different sources to determine the site of new outbreaks of dengue fever.

Dengue is an illness that a huge 40% of the population are at risk of contracting, and with a huge 390 million cases each year, according to statistics from the WHO. The virus's reach already stretches all the way from Central and South America, through Africa and Asia right over to Australia. And as temperatures rises, the number of cases is increasing.

The system developed by AIME analyses not only public health data but other data from other sources, such as weather, wind speed, previous outbreaks and a location's proximity to large bodies of water - anything which might influence the behaviour of the mosquitoes that carry the disease. They also look at things like population density in the area, peoples' health records and their income level.

The project started in Malaysia and recently spread further afield, with the AIME team collaborating with NGOs and local governments in Brazil before the Olympic Games in 2016, to carry out a pilot programme and develop preventative strategies to halt the spread of zika and dengue.

Their system claims to be able to provide the exact geolocation and date of the next dengue outbreak, up to three months in advance and can recommend anti-dengue measures for the area within a 400-meter radius - including genetically modified mosquitoes and so-called fogging.

"Dengue is just the start," Rainer Mallol says. "We will create a device to diagnose tuberculosis and malaria. We will create another software to diagnose diabetic retinopathy (a disease which can lead to blindness)." There's also an idea "to link blood banks all over the world."

It's clear Mallol and AIME have no limits to their ambitions. Though for now, Dengue remains the focus. "My invention will change the future," he says. "I know this because it's already changing the future in Malaysia."

Artificial intelligence and machine learning in healthcare will continue to get better and impact disease prevention and diagnosis, extract more meaning from data across various clinical trials, help develop customized drugs based on an individual’s unique DNA and inform treatment options among other things.