Facial recognition is a technology used to identify a person based on their facial features. This AI technique measures features like bone structure, the distance between the eyes, etc and produces a mathematical model that is unique to each individual.
The top facial recognition applications have mostly been within security and cybersecurity but what about the potential this technology holds for healthcare? Facial recognition medical applications might hold the key to better diagnosis, end of ethnic discriminations, etc.
In this article, together with InData Labs, facial recognition app development company, we’ll examine six real-life applications of AI in the medical field.
Early diagnosis at home
More and more people are taking preventative healthcare into their own hands. They seek different ways they can get a health update without necessarily waiting to get an appointment with their GP. In 2020 alone, the wearable tech market grew by 30%.
Few companies are serving that market by using machine-learning algorithms to make face recognition technology more accessible to people. With a simple camera that is either in your phone or even built into a mirror, you can undergo simple skin analysis, measure blood pressure and stress level.
The technology behind this is based on simple image analysis that can recommend treatments, medications, or even refer you to a doctor if needed based on how you look like. This can be vital in the early diagnosis of multiple diseases such as skin cancer, high blood pressure, or even burnout.
Proper patient identification in emergencies
According to research from the University of Nebraska, poor healthcare delivery is often due to the absence or inaccessibility of a patient’s medical history. This a substantial challenge for rural areas where medical history isn’t digitalized or simply unavailable. But this is an even bigger problem when the patient is in need of emergency care.
When a person is badly injured, they are often unable to provide any identification. This poses a great challenge for healthcare personnel when time is of the essence. They need to make fast decisions about what care to administer a person without knowing the details of the patient data, such as medications they take, past blood samples, MRI, or CT scans.
With a mobile device equipped with a face recognition system, however, healthcare professionals can quickly access medical journals just by scanning the face of a person. This, of course, requires a bigger technical infrastructure to be put in place where medical records across healthcare points are digitized and shared.
Automating fraud prevention
Face recognition isn’t just useful to treat patients. It can also be applied to a video surveillance system across healthcare facilities as a non-invasive way to prevent fraud.
Facial recognition in the medical sector can help detect and identify people who have a history of drug abuse or insurance fraud. The same can be applied to those wanted by authorities for violent crimes. With a non-invasive system like that, the presence of security and law enforcement can be minimized, and they can deal with any potential threats without disturbing the patients.
The same system can also be applied for first-time patients that visit a healthcare facility. In such cases, they can be directed to a different place or asked to follow a different routine much faster than through a reception.
A few companies are even working on developing face recognition technology that works with eyes only, since masks are an everyday thing in the healthcare industry.
Optimizing pain relief
Another application of Artificial Intelligence can be found in aftercare delivery, namely pain management. More often than not patients leave the hospital or the doctor’s office with a prescription or even a pump that distributes painkillers.
During post-surgery recovery, patients may not be fully capable of administering the right amount of medicine. Both overdose and underdose of medicine can have serious side effects and prolong recovery time. Moreover, patients with previous drug addictions might be at risk of relapse when given access to medications.
AI based monitoring of patients’ facial expressions in real-time combined with dose monitoring can enable healthcare professionals to calculate and deliver the right doses while minimizing the above mentioned risks. Furthermore, this can be done remotely, saving time both for the patients and the healthcare professionals.
Prevent cybercrime targeting medical data
AI applications in the medical field aren’t always that different from other industries, especially when it comes to cybersecurity.
Medical records are one of the most sought-after data for cybercriminals. Full medical records can allegedly be sold for up to $1.000, according to a news report from CBS News. These records often contain Social Security numbers and credit card information – two pieces of data that can be used for identity theft and financial scams.
One of the biggest security holes in a hospital is, in fact, passwords healthcare personnel use to access and alter medical records. They are easy to hack because busy nurses and doctors typically use a password they can easily remember during a hectic day. With face recognition, on the other hand, healthcare personnel can skip passwords and use their face instead.
AI systems like these can also protect healthcare professionals from getting hacked in other places, if they use their work-related password other places, such as email, bank, etc.
Reduce burnouts and depression among staff
Not all facial recognition technologies for digital healthcare are about treating patients. Some are made with healthcare professionals in mind.
Burnout is one of the most common psychological diseases among healthcare professionals. According to a survey from Medscape as many as 44% of doctors were feeling burned out, 11% were colloquially depressed, but only 4% were actually diagnosed with clinical depression.
With the help of machine learning algorithms and neural networks, medical facilities can monitor their staff in a non-invasive way, and pick up early signs of depression and burnout. As a result, systems can be put in place, and even automated, to schedule more vacation, facilitate more frequent breaks.
Medical facilities can also put in practice preventative practices, such as meditation or recreational activity bonuses, when they are working with early signs of depression and burnout. In turn, healthcare professionals will be able to provide better care and decrease medical errors.