The Prospects of AI Facial Scanning In Healthcare

Studies conducted by medical scientists and researchers in nations around the world target the influence of AI in the broad sub-fields of medicine and healthcare. According to their studies, AI will become an integral part of long-term success: by efficiently engendering newfound medical equipment, and by bolstering patient and professional interaction. Today, this long-term success has manifested itself in a variety of processes and applications. One example, as of recent news, is the ability of AI in diagnosing genetic disorders, just by examining an individual's face.

Examples of faces associated with certain syndromes.

Examples of faces associated with certain syndromes.

The process appears quite simple; yet, the efficiency this technology can provide to any health care worker is unmatched. Recently, a study was conducted by Yaron Gurovich and a team of technology officers at GNDA. This new prototype of artificial intelligence, named DeepGestalt, had not been tested, and it was Gurovich's goal to evaluate its productivity in real-time medical scenarios. The foundation of his study included 17,000 photographs of human beings - with a mix of diseased and entirely healthy individuals. Yet, to provide a challenge for AI, each individual had already been matched with their genetic disorder or state of health. A grand total of over 200 genetic disorders were listed in the platform, drawing distraught from Gurovich, who claimed: "I felt as if I had taken it too far."  

To commence the study, AI was employed in a rapid-fire process, scanning and analysing the faces of the individuals. Each face was stored in the memory of DeepGestalt, which had room for over 100,000 faces and could be cleared on demand at any time. After less than thirty minutes, the evaluation was complete and Gurovich was prepared to evaluate the results. He found that AI was correct in its diagnosis 91% of the time, and had perfectly identified over 15 genetic disorders, including Turner Syndrome, Progeria, Triple x syndrome and Tay-Sachs disease. Furthermore, with one of the disorders that medical professionals themselves have trouble in diagnosing, Noonan syndrome, DeepGestalt correctly identified it 64% of the time, rivalling the successes of medical professionals in much less time.

How the AI interprets scanned faces.

How the AI interprets scanned faces.

Incorporated with further modifications and the aid of medical professionals, DeepGastalt could make its way into every hospital worldwide. As Gurovich claims, "It's clearly not perfect, but it's much better than humans are at trying to do this." Yet, in addition, with these modifications, it is very probable that a new definition of medical standardization will become adopted by a greater quantity of doctors and nurses in diagnosis. Specifically, health professionals will reconsider their craft in examining visual signs of disease. A common practice by doctors today is to evaluate specific parts of the human body at first in the diagnosis of disorders, such as their arms or legs, without much regard to the rest. With the proven success of DeepGastalt, doctors may consider commencing by analyzing an individual's entire body, with a larger focus on the face, on the spot, or through a photograph, in order to thoroughly cooperate with and verify the results of DeepGastalt.

Thus, it is inevitable that AI will largely impact the diagnosis process for both medical professionals and patients. Beyond Gurovich, other teams of scientists, including FDNA, have performed authorized experiments on the influences of DeepGastalt - and found similar results that indicate its success. The potential these entail for the medical sector are insurmountable, and will prove beneficial for the greater good of humanity. Albeit our doctors, nurses and professionals are performing excellently, the additional support of AI can be the missing block in extending healthcare's reach farther into the future.