NEW YORK, NY / ACCESSWIRE / November 12, 2020 / COVID-19 has undoubtedly accelerated the application of AI in healthcare, such as virus surveillance, diagnosis and patient risk assessments. AI-powered drones, robots and digital assistants are improving healthcare industry with better accuracy and efficiency. These have enabled doctors to provide more effective and personalized treatment with real-time data monitoring and analysis.
Garbage in, garbage out
As one of the most popular and promising subsets of AI, machine learning gives algorithms the ability to 'learn' from training data so as to identify patterns and make decisions with little human intervention. However, as the saying goes, 'garbage in, garbage out,' making sure correct data fed into ML algorithms is not an easy work.
According to a report 'the Digital Universe Driving Data Growth in Healthcare,' published by EMC with research and analysis from IDC, hospitals are producing 50 petabytes of data per year. Almost 90% of this data consists of medical imaging i.e. digital images from scans like MRIs or CTs. However, more than 97% of this data goes unanalyzed or unused.
Unstructured raw data needs to be labelled for computer visions so that when the data is fed into an algorithm to train a ML model, the algorithm can recognize and learn from it. As DJ Patil and Hilary Mason write in Data Driven, 'cleaning and labeling the data is often the most taxing part of data science, and is frequently 80% of the work.'
Many enterprises wish to apply AI to their business practices. They have a glut of data, such as vast amounts of images from cameras and document texts. The challenge, however, is how to process and label those data in order to make it useful and productive. Many organizations are struggling to get AI and ML projects into production due to data labeling limitations and real-time validation deficiency.
A robust data labeling platform with real-time monitoring and high efficiency
An entire ecosystem of tech startups has emerged to contribute to the data labelling process. Among them, ByteBridge.io, a data labeling platform, solves the data labeling challenge with robust tools for real-time workflow management and automating the data labeling operations. Aiming at increasing flexibility, quality and efficiency for the data labeling industry, it specializes in high volumes, high variance, complex data, and provides full-stack solution for AI companies.
'On the dashboard, users can seamlessly manage all projects with powerful tools in real-time to meet their unique requirements. The automated platform ensures data quality, reduces the challenge of workforce management and lowers the costs with transparent standardized pricings,' said Brian Cheong, CEO and founder of ByteBridge.io.
The quality of labeled dataset determines the success of AI projects, making it vital to look for a reliable platform that can help developers to overcome the data labeling challenges. The demands of data labelling will continue to be on the rise with the development of AI programs.
Human beings benefit from the implementation of AI systems into medical industry: from diagnosis to treatment, from drug experiment to generalization. These are all exciting areas for AI developers. But before that, providing high-quality training data lays the cornerstone of making those progress.
SOURCE: TTC Foundation
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