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Colorectal Cancer Detection with Latest AI Technologies
Integration of advanced and new Artificial Intelligence technologies in cancer treatment has the potential to increase diagnostic accuracy and efficiency, as well as clinical decision-making and health outcomes.
By: Cogitotech brand of data annotation company.
AI integration in cancer care might enhance diagnostic accuracy and speed, help clinical decision-making, and result in improved health outcomes. Clinical treatment driven by advanced and new Artificial Intelligence technologies has the potential to make a significant difference in eliminating health inequities, especially in low-resource settings.
According to the artificial intelligence latest news, researchers from Tulane, Central South University in China, the University of Oklahoma Health Sciences Center, Temple University, and Florida State University collaborated on the study, which aimed to see if artificial intelligence latest trends could help pathologists keep up with the growing demand for their services.
Regularly, pathologists review and labelled hundreds of histopathology images to determine whether or not someone has cancer.
"We haven't marketed it yet because we need to make it more user-friendly and test and use it in more clinical situations,"
The study indicated that the average pathologist scored .969 for manually diagnosing colorectal cancer after comparing the computer's results with the work of highly skilled pathologists who analyzed data manually. The average score for the AI computer program with machine assistance was .98, which is comparable if not more accurate.
Apart from this, various research is evaluated for the various sorts of systems utilized for cancer early detection. Automated or computer-aided systems using artificial intelligence (AI) are being investigated because they are a great fit for processing a huge dataset with accuracy and efficiency in cancer detection. These systems can be used to aid in diagnosis and therapy.
About Cogito Company
Cogito is a hybrid data labelling platform following model-assisted labelling (MAL) approach to cater industry's leading businesses. The MAL model leverages a human workforce to label a relevant portion of the training dataset which enables training of the AI application. Playing an important part as human-in-the-
Roger Brown (Cogito) Medical Image Data Annotation