Predicting where Ebola might strike next could become easier, thanks to a new computer model. The model tracks how changes in the environment and in human societies could affect the deadly virus's spread. It predicts that Ebola outbreaks could become as much as 60 percent more likely by 2070 if the world continues on a path toward a warmer climate and a cooling economy. Ebola, on average, kills half of all people who contract the virus. In previous outbreaks, the fatality rate has risen to as high as 90 percent.

The almighty dollar is a powerful factor in the current wave of artificial intelligence (AI) adoption. And why shouldn't it be? For millennia, companies have relied on technological progress to grow sales, cut costs, and improve customer satisfaction. But if we take a wider view, we see there is tremendous potential for AI to benefit society as a whole. Unfortunately, these "AI for good" projects often face big obstacles to success.

You could argue that our failure to focus on what's getting better suggests that the media generally is missing an enormous story. News by its nature is about a surprise. Which day do you cover malaria deaths being cut in half? Which day do you cover workplace accidents down by a factor of 50 over the 50-year period? It's society doing what it's supposed to do.

AI also raises the prospect of affordable healthcare for all. According to the World Health Organization (WHO), 400 million people do not have access to one or more essential health services, and 6% of those in low and middle-income countries are pushed further into extreme poverty because of health spending. In the future, we will see physicians working in partnership with AI – enabling technology to free up their time to concentrate on treatment of the disease as opposed to the diagnosis. Here we look at areas where AI promises to have a real impact on chronic and infectious diseases, from diagnosis and treatment plans to containing the global outbreaks of the likes of SARs and Ebola. Nearly 18 million people die each year from cardiovascular disease, according to WHO.

The lifesaving drugs of the future may very likely be designed by robots. With the ability to test one million new compounds per day, this AI system demonstrates why that's a good idea. Artificial intelligence is helping transform every aspect of our lives, and drug discovery is no exception. AtomNet, a system created by San Francisco-based startup Atomwise, is designed to help with the goal of curing major diseases by predicting the bioactivity of small molecules using a deep learning neural network. New drugs, invented by robots.

We haven't figured out what Ebola virus selects as its natural host, but it's definitely not humans. Every once in a while, Ebola stumbles upon a human host, which ends up being a fatal mistake. When I say fatal, I mean for the virus. After all, Ebola is usually not highly efficient at sustaining infection or transmitting from human to human, and eventually that chain of transmission turns into a dead end. Every Ebola outbreak has ended, even the 2014-2015 West African epidemic.

Over the course of the past year or so, there have been a number of incredible tech projects aimed at stopping the spread of Ebola. One approach that we've never come across before, however, involves plotting the possible spread of Ebola and other "filoviruses" of the same family by predicting which bat species they're most likely to be carried by. That's exactly the goal of a team of scientists, who recently used machine learning techniques to build just such a model. Their work may help prevent future spillover events in which it is important to predict which species of wildlife help spread contagion. "This work entailed collecting intrinsic features describing the world's bat species -- 1,116 species altogether -- and training a machine learning algorithm on these data to learn which features best predict the bat species that carry filoviruses," lead author of the study Barbara Han, a disease ecologist at the Cary Institute of Ecosystem Studies, tells Digital Trends.

When you get right down to it, developing vaccines is about data and luck. Scientists start with a set of variables--what drugs a virus responds to, how effectively, and for whom--and then it's a whole lot of trial and error until they stumble upon a cure. One of the most exciting possibilities in medical research right now is how technology like machine learning could help researchers rapidly process those enormous sets of data, more quickly leading to cures. This is already starting to happen: In a study published Wednesday in the journal Macromolecules, researchers from IBM and Singapore's Institute of Bioengineering and Nanotechnology reveal a breakthrough that could help prevent deadly virus infections. With the help of IBM super computer Watson, they hope their finding will soon make its way into vaccines.