Advancements in the scientific and robots industry can lead to many new breakthroughs. The University of Maryland is making a fair bit of headlines in this regard. More specifically, one of its engineers is in favor of equipping all ambulances with medical robots to haul trauma patients. These robots will gain additional knowledge through machine learning.

Robots Will Save Human Lives, Eventually

A lot of people are still confused when it comes to potentially having robots

To put this in perspective, there is a certain risk factor associated with the ride to the hospital as a trauma patient. It is estimated one-third of trauma fatalities could be prevented if the patient had access to a higher level of care sooner. Despite this knowledge, it is very difficult to make something these lines happen. Ambulances are equipped with most of the necessary tools to provide the first care, but if a patient is already in critical condition, these “tools” are not always sufficient.

To counter this problem Assistant Professor Axel Krieger suggests a radical approach. He is confident equipping ambulances with a medical robot enhanced by machine learning is the only path forward. Especially when it comes to internal bleeding or other injuries below the skin, failing to diagnose these conditions can threaten patients’ lives. Since these conditions cannot be assessed accurately, having a medical robot on board can certainly improve upon the situation.

Krieger explains it as follows:

“Imagine you have a patient in the emergency vehicle, and a robot scans the patient and obtains ultrasound images. This can provide a critical level of life-saving diagnosis and care not yet possible during an emergency ambulance ride.”

These medical robots will scan and visualize potential injuries and compare the data with its built-in machine learning algorithm. The robots will primarily focus on anatomic areas known to be vulnerable to hidden injuries and bleeding.  By properly analyzing all of the characteristics of these injuries, a more complete injury profile can be compiled. This will improve the trauma patient’s chances of survival, as they will receive appropriate care as soon as possible.

The big hurdle will come in the form of developing an appropriate machine learning system to power the robot. A collaboration is underway, albeit it is still in the experimental stage at this time. Nor is it approved for clinical use with patients, but that situation can change in the near future. The whole concept of equipping ambulances with medical robots makes a lot of sense on paper. However, in the real world, such ideas will always be met with a fair bit of scrutiny first and foremost.