It was an AI that first saw it coming, or so the story goes. On December 30, an artificial-intelligence company called BlueDot, which uses machine learning to monitor outbreaks of infectious diseases around the world, alerted clients—including various governments, hospitals, and businesses—to an unusual bump in pneumonia cases in Wuhan, China. It would be another nine days before the World Health Organization officially flagged what we’ve all come to know as Covid-19.

BlueDot wasn’t alone. An automated service called HealthMap at Boston Children’s Hospital also caught those first signs. As did a model run by Metabiota, based in San Francisco. That AI could spot an outbreak on the other side of the world is pretty amazing, and early warnings save lives.

But how much has AI really helped in tackling the current outbreak? That’s a hard question to answer. Companies like BlueDot are typically tight-lipped about exactly who they provide information to and how it is used. And human teams say they spotted the outbreak the same day as the AIs. Other projects in which AI is being explored as a diagnostic tool or used to help find a vaccine are still in their very early stages. Even if they are successful, it will take time—possibly months—to get those innovations into the hands of the health-care workers who need them.

The hype outstrips the reality. In fact, the narrative that has appeared in many news reports and breathless press releases—that AI is a powerful new weapon against diseases—is only partly true and risks becoming counterproductive. For example, too much confidence in AI’s capabilities could lead to ill-informed decisions that funnel public money to unproven AI companies at the expense of proven interventions such as drug programs. It’s also bad for the field itself: overblown but disappointed expectations have led to a crash of interest in AI, and consequent loss of funding, more than once in the past.

So here’s a reality check: AI will not save us from the coronavirus—certainly not this time. But there’s every chance it will play a bigger role in future epidemics—if we make some big changes. Most won’t be easy. Some we won’t like.

There are three main areas where AI could help: prediction, diagnosis, and treatment.

Prediction

Companies like BlueDot and Metabiota use a range of natural-language processing (NLP) algorithms to monitor news outlets and official health-care reports in different languages around the world, flagging whether they mention high-priority diseases, such as coronavirus, or more endemic ones, such as HIV or tuberculosis. Their predictive tools can also draw on air-travel data to assess the risk that transit hubs might see infected people either arriving or departing.

The results are reasonably accurate. For example, Metabiota’s latest public report, on February 25, predicted that on March 3 there would be 127,000 cumulative cases worldwide. It overshot by around 30,000, but Mark Gallivan, the firm’s director of data science, says this is still well within the margin of error. It also listed the countries most likely to report new cases, including China, Italy, Iran, and the US. Again: not bad.