It’s inevitable. AI is going to change in healthcare as we know it. But in this article, I want to show you a few medical specialties where it’s already making a difference. There are some Artificial Technology(AI) technology start using in the Healthcare sector.
In 2018, a deep-learning-based algorithm was developed using more than 50 thousand normal chest images and almost 7 thousand scans with active TB. The algorithm became so good that in performance tests it easily beat radiologists. Of course, it has its shortcomings, but this test shows that even the AI of today can be a helpful second reader for physicians, while the A.I. of tomorrow can bring screenings and precise diagnostics to even less developed and rural areas where medical professionals are not available. It helps in AI in Healthcare.
AI is advancing elsewhere too. Researchers in Germany, the US, and France trained a deep learning neural network to identify skin cancer by feeding it with more than 100,000 images of malignant melanomas and benign moles. After its training, they compared its performance with 58 international dermatologists and the results were remarkable. While the dermatologists accurately detected more than 86% of the melanomas, the neural network detected 95% of them.
One of the biggest promises of A.I. is that one day it could crack the code of individualized cancer diagnosis and treatment. And Watson, IBM’s very own AI, is a powerful tool that is mainly being used and tested in the field of Oncology in healthcare. So far, dozens of hospitals have adopted this technology and it’s been used in conjunction with medical judgment. While its promise is strong, it has not yet been able to live up to the expectations in the fight against cancer.
Cardiovascular diseases are the number one cause of death globally. For those affected, early detection is critical for both management and treatment. And in the future, AI-based predictions could be a life-saver. Since studies have shown that markers of cardiovascular disease can often manifest in the eye, scientists are using deep-learning methods to identify risk factors such as age, gender, smoking status, and blood pressure only by looking at the eye.
These new studies still need to be validated and repeated on more people before they could gain broader acceptance, but since retinal images can be obtained quickly, cheaply, and non-invasively, this will probably open new horizons in healthcare, and AI also has several limitations. Most of these studies have not been tested under clinical circumstances and algorithms are only precise in a specific task while clinical life is much more diverse. Nevertheless, what matters here is that AI has amazing promise.