Artificial Intelligence in Medical Diagnosis

Artificial Intelligence in Medical Diagnosis

Artificial Intelligence in Medical Diagnosis

By Adrián Savarese

The field of artificial intelligence moves fast. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition.

Correctly diagnosing diseases takes years of medical training. Even then, diagnostics is often an arduous, time-consuming process. Machine Learning have recently made huge advances in automatically diagnosing diseases, making diagnostics cheaper and more accessible.

Machine Learning algorithms, particularly Deep Learning algorithms, can learn to see patterns similarly to the way doctors see them from large amounts of data. A key difference is that algorithms need a lot of concrete examples in order to learn.

So Machine Learning is particularly helpful in areas where the diagnostic information a doctor examines is already digitized, such as:
Detecting lung cancer or strokes based on CT scans, assessing the risk of sudden cardiac death or other heart diseases based on electrocardiograms and cardiac MRI images, classifying skin lesions in skin images, finding indicators of diabetic retinopathy in eye images, etc.

AI will not replace doctors. Quite the opposite, AI systems will be used to highlight potentially malignant lesions or dangerous cardiac patterns for the expert – allowing the doctor to focus on the interpretation of those signals.

Our engineers at SimTLiX have strong experience using Machine Learning and Artificial Intelligence for medical solutions adding value to healthcare products and services, making a key contribution to your business.

We invite you to visit our website section related to Machine Learning and Artificial Intelligence