The Future of Machine Learning in Healthcare
Updated: Jun 17, 2020
Artificial intelligence (AI), big data and machine learning (ML) were some of the biggest buzzwords of 2019, and remain strong into the new year. These technologies work together to figure out patterns in datasets and predict outcomes of new data, to be used among others in FinTech, marketing, retail, and healthcare. In the past approximately five years, ML has become astoundingly good at analyzing images, revolutionizing diagnostic imaging procedures in healthcare - sometimes spotting irregularities in scans at higher accuracy than trained professionals.
AEYE Health is one of the companies at the forefront of intelligent healthcare, having developed algorithms which are able to automatically detect most major retinal diseases. Thanks to a growing database of hundreds of thousands of annotated retinal images, AEYE Health’s automated diagnostic system is predicting results at high accuracy.
Overall, ML in healthcare is an incredible development that will increase efficiency and accuracy in disease detection. In many cases, it will also enable early discovery and treatment accessibility in remote or developing places, as ML can significantly reduce costs and necessity for doctor appointments. Finally, health systems around the world are becoming overburdened as the population grows and life expectancy rises, and automated systems can reduce this burden.