While the results were not as robust as those typically seen in journal publications, the study is one of the first to demonstrate the feasibility of using sensors and algorithms to understand what’s happening in the ICU. “A lot of people might not have even thought this is possible at all,” says Topol. “A patient’s room is kind of like Grand Central Station. There’s so many things going on.”

The demonstration suggests how these systems might augment the work of hospital staff. If algorithms can track when a patient has fallen or even anticipate when someone is starting to have trouble, they can alert the staff that help is required. This could spare nurses the worry provoked by leaving one patient alone as they go on to care for another.

But what makes the study even more notable is its approach. Much AI research today focuses purely on advancing algorithms out of context, such as by fine-tuning computer vision in a simulated rather than live environment. But when dealing with sensitive applications such as health care, this can lead to algorithms that, while accurate, are unsafe to deploy or do not tackle the right problems.

In contrast, the Stanford team worked with medical professionals from the very beginning to understand what they needed and reframe those needs as machine-vision problems. For example, through discussions with the nurses and other hospital staff, the AI researchers concluded that using depth sensors rather than regular cameras would protect the privacy of patients and personnel. “The clinicians I worked with—we discussed computer vision and AI for years,” says Serena Yeung, one of the lead authors on the paper, who will become an assistant professor of biomedical data science at Stanford this fall. “Through this process, we were able to unearth new application areas that could benefit from this technology.”

The approach meant the study went slowly: it took time to get buy-in from all levels of the hospital, and it was technically complex to analyze the hectic, messy environment of the ICU while using only silhouette data. But taking this time was absolutely critical to design a safe, effective prototype of a system that will one day be genuinely beneficial to the patients and care staff, says Yeung.

Unfortunately, the current culture and incentives in AI research do not lend themselves to such collaborations. The pressure to move fast and publish quickly leads researchers to avoid projects that don’t produce immediate results, and the privatization of a lot of AI funding hurts projects without clear commercialization opportunities. “It is rare to see people working on an end-to-end system in the real world, and also spending the many years that it takes and doing the grunt work that is required to do this type of impactful work,” says Timnit Gebru, co-lead of the Ethical AI Team at Google, who was not involved in the research.

Fortunately, a growing number of experts are pushing to change the status quo. MIT and Stanford are each opening new interdisciplinary research hubs with a charge to pursue human-centered, ethical AI. Yeung also sees opportunities for algorithmically focused AI conferences like NeurIPS and ICML to partner more closely with researchers who focus on social impact.

Topol is optimistic that deeper collaboration between the AI and medical communities will bring forth a new standard of health care. “We’ve never had truly patient-centered care,” he says. “I hope we will get there with this technology.”

This story originally appeared in our AI newsletter The Algorithm. To have it directly delivered to your inbox, sign up here for free.