Artificial Intelligence

Optimizing lab operations with the power of AI

Learn how colleague Rayal Raj Prasad is optimizing lab diagnostics with the help of artificial intelligence.
Rebecca Murr
Published on 7. Dezember 2022

What does the work of an artificial intelligence (AI) research scientist look like? Meet our colleague Rayal Raj Prasad who sits at the intersection of science and technology. With the help of artifical intelligence, he intends to solve challenges in laboratory diagnostics. 

AI-powered solutions can help automate and standardize not only workflows but also complex diagnostics to meet patients’ needs. Together with super-fast cloud computing and robotics, they can also optimize laboratories, for example by automating sample processing. As the demand for testing keeps increasing labs are getting larger and more complex to operate. To keep up with this testing demand labs need to analyze and optimize their operations based on the data streams being generated.
This is exactly what Rayal Raj Prasad, an AI research scientist at Siemens Healthineers, wants to advance.
While working on his master’s degree in mechanical engineering at Columbia University in New York, Rayal specialized in robotics. He was eager to see how intelligent robotics could be used to automate and improve healthcare devices and workflows. The Automation Research Group at Siemens Healthineers in Princeton, USA, finally provided him this opportunity.
He now uses his knowledge to create an intelligently connected lab that will truly transform patient care with the help of AI techniques.
The biggest benefits of AI are integrating health records, symptom profiles, demographic data, and other patient information to better recommend diagnoses and treatments. Rayal’s vision is clear: enabling laboratories to work more efficiently through AI-driven automation of highly complex diagnostics systems.

He enjoys one thing in particular: running large-scale simulations of labs on the Sherlock supercomputer. “These simulations generate billions of data points on which we train our intelligent cooperative agents,” he explains. These agents collaboratively make decisions about lab operations such as tube routing and workflow assignments, all to optimize the overall operation of the lab.
This immense scale is something that motivates Rayal each and every day.
“Even a small improvement in operations could lead to thousands of patients getting their test results faster,” he says. “These could be crucial for critical or pediatric patients where every second matters.”

By Rebecca Murr

Rebecca Murr is an editor at Siemens Healthineers.