New mobile medical diagnosis tool leverages machine learning for healthcare innovation
In healthcare, mobile innovation represents an opportunity to save lives and improving the quality of care for countless patients. A new mobile solution promises significant changes to how physicians approach medical diagnosis, likely resulting in more timely diagnoses to give medical experts a better shot at catching disease early and administering treatment.
Researchers at UCLA have developed a tool that leverages machine learning to power a smart mobile device designed for the healthcare world. More specifically, this piece of technology can detect proteins, viruses, cancer biomarkers and other microscopic bodily objects that have traditionally been difficult for researchers to find in patients, according to Phys.org.
Researchers believe that, through machine learning, this smart solution can train its own algorithms to process biometric data and better understand biomarkers and other biological trends. As a result, viruses and diseases could be caught earlier; an individual with an early-stage cancer, for example, could be diagnosed by this smart technology while the cancer is still too small to be detected through other traditional methods.
Even better for healthcare companies is the cost: This new solution is purported to be far less expensive than traditional devices used for medical diagnosis, which could facilitate faster adoption throughout the healthcare industry.
It’s possible that the lightweight, portable device could be enhanced even further to maximize adoption while leveraging mobile connectivity. The researchers suggest that a mobile phone attachment could be designed that would lower costs even more while giving healthcare practitioners the ability to use cloud-based solutions in conjunction with the sensor. The sensor searches for these biomarkers through the use of a plasmodic reader, which wields disposable microchips to sample blood, urine or other bodily fluids.
While innovations are taking place left and right for the healthcare industry, this new solution is garnering additional interest because of how practical it could be, and how quickly it could simplify diagnosis while also improving providers’ ability to identify and treat diseases sooner.