Robotic surgery systems are used in thousands of hospitals around the world. Ten years ago, these were clunky machines designed to facilitate routine procedures. Today, they are able to perform end-to-end surgeries without human assistance.
Recent advances in deep learning have made difficult tasks such as surgery, electronics assembly, and piloting a fighter plane relatively straightforward. It can take a decade to train a human in all the medical knowledge necessary to perform brain surgery. And this cost is the same for each subsequent human surgeon thereafter. It takes roughly the same investment for every human surgeon.
But AI is different. The initial investment to create a robotic surgical device can be significant, but that all changes once you produce a working model. Instead of 8 to 12 years to create a human specialist, factories can be built to mass produce AI surgeons. Over time, the cost of maintaining and operating a surgical machine – a machine capable of running 24/7, 365 days a year without paying a salary – would likely become insignificant compared to maintaining of a human surgical staff.
That’s not to say there isn’t a place for human surgeons in the future. We will always need human experts who can inform the next generation of machines. And some procedures remain beyond the capabilities of modern AI and robotics. But surgery, just like any other precision-driven business, is definitely a domain of modern AI.
Surgery is a specific skill, and for the most part, robots excel at automating tasks that require more precision than creativity. And that’s exactly why robotic surgeons are rife, but we’re probably decades away from a fully functional nurse powered by AI.
And that’s exactly why AI didn’t have a huge impact during the pandemic. When COVID-19 first hit, there was a lot of optimism that big tech would save the day with AI. The idea was that companies like Google and Microsoft would come up with incredible contact tracing mechanisms that would allow us to tailor medical responses at an extremely granular level. We collectively believed this would lead to a truncated pandemic.
We were wrong, but only because there was really nothing for the AI to do. Where it could help, by contributing to the rapid development of a vaccine, he did. But the vast majority of our problems in hospitals were related to things that a modern robot cannot fix.
What we needed at the last peak of patients were more nurses and PPE for them. Robots can’t look around and learn like a human, they have to be trained exactly what they’re going to do. And that’s just not possible during giant emergencies where, for example, a hospital’s floor plan changes to accommodate an increase in the number of patients and massive amounts of new equipment are introduced.
Researchers at Johns Hopkins University recently conducted a study to find out what we need to do to get robots to help healthcare professionals in future pandemics. According to them, modern robots are not up to the task:
A big issue has been the ability to deploy and how quickly a non-expert user can customize a robot. For example, our ICU ventilation robot was designed for a type of fan pushing on buttons. But some fans have buttons, so we need to be able to add a modality so that the robot can also manipulate the buttons. Suppose you want a robot capable of serving multiple fans; then you would need a mobile robot with an arm attachment, and this robot could also do a lot of other useful work on the hospital floor.
All is well when things are going perfectly. But what happens when the button pops off or someone introduces a new kind of rocker or touchscreen machine? Humans have no problem adjusting to these situations, but a robot would need a brand new prop and a training update to make up for it.
In order for developers to create a “nursing robot,” they would need to anticipate everything a nurse encounters on a daily basis. Good luck with that.
AI and machines can be adapted to perform certain Tasks related to nursing care, such as assisting with admission or recording and monitoring patient vital signs. But there isn’t a machine in the world that can perform the day-to-day routine functions of a typical hospital staff nurse.
Nurses spend most of their time reacting to real-time situations. In a given shift, a nurse interacts with patients, sets up and dismantles equipment, manipulates precision instruments, carries heavy objects through spaces filled with people, solves mysteries, takes meticulous notes, and acts as a liaison between medical staff and the general public.
We have the answer to most of these problems individually, but bringing them together in a mobile unit is the problem.
This Boston Dynamics robot that performs backflips, for example, could certainly navigate a hospital, transport things, and avoid causing injury or damage. But he has no way of knowing where a doctor accidentally left the board he needs to update his diaries, how to calm a frightened patient, or what to do if a motionless patient misses the bedpan.
Published March 30, 2021 – 17:58 UTC