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AI has the potential to transform healthcare. Be it predicting the risk of terminal diseases or developing novel drugs, companies are leveraging data-driven algorithms to improve the quality of patient care in every way possible. The use cases are only expected to grow from here, but there are also certain hurdles along the way. Case in point: The lack of high-quality datasets.
Health organizations cumulatively generate about 300 petabytes of data every single day. This information is stored across systems yet not effectively used due to poor preparation. Basically, data teams, which tend to create manual rules for data cleanup, are struggling to keep up with the growing volumes of information. They spend most of their time, almost 80%, on getting the data ready – making it accurate, connected and standardized – rather than actually exploring and analyzing it for potential, life-saving AI applications.
Cornerstone AI’s comprehensive solution
To solve this, San Francisco-based company, Cornerstone AI, has launched a solution that automatically characterizes, harmonizes and cleans healthcare data in a fraction of the time taken by traditional methods. The company also announced it has raised $5 million in seed funding.
According to Cornerstone, the algorithm of its platform uses a combination of custom Python and R code to scan each table and data point — inferring their structure and validity — and then organizes the tables for analysis while removing and correcting all notable errors.
“A data team doesn’t have to configure anything in the sy …