Diagnostic data company Diaceutics is banking on artificial intelligence to help improve patient testing.
The company, which purports to help patients receive potentially lifesaving medicine through better diagnostic testing, is collaborating with computer hardware manufacturer Lenovo, and chip manufacturer Intel to leverage their vast proprietary database of patient testing data in a new manner. Diaceutics is using AI to see whether patients can be grouped according to diagnostic test information, in the hopes that this data can help identify ways to improve diagnosis, treatment and outcomes for patients with similar characteristics.
Diaceutics is utilizing Lenovo’s ThinkStation P920 powered by Intel Xeon Scalable processors, and a vast amount of complex testing data from hundreds of clinical laboratories. After a number of biomarkers covering a variety of tumors and cancer types have been inputted, the machine learning system will be able to identify similarities and patterns within the testing data and highlight what is happening among groups of patients.
The AI-based system will purportedly be able to organize information in manner that more readily identifies at-risk patients more quickly, thus making it possible to diagnose and treat them much faster.
Diaceutics believe that giving patients greater access to potentially lifesaving medication can help stem the tide of tens of thousands of patients receiving inadequate testing annually for diseases such as cancer. The company believes the collaboration could further transform the area of personalized medicine.
Peter Keeling, chief executive officer, Diaceutics said in a statement, “Artificial intelligence can make a very strong and positive impact on precision medicine and we are excited to be moving the boundaries with this highly innovative technology. We can now look at data in a novel way. No longer will we only be making decisions based on diagnostic data. Artificial intelligence allows us to look at the complete patient journey from initial testing and diagnosis, and on to the ultimate treatment. The result will be significantly better patient testing and likely improved patient outcomes, such as longer cancer survivorship rates.”
