Meet a Siemens Healthineers Data Scientist working in AI: Ren-Yi Lo

In order to develop artificial intelligence (AI) for use in medical technology, Siemens Healthineers research scientists need one thing above all: colossal quantities of all kinds of correct and secure medical data. Ren-Yi Lo, Head of Siemens Healthineers Big Data Office, and is in charge of collecting, preparing, and organizing these data.

AI is a crucial pillar for the future of medical technology: For example, it can automate routine processes along the clinical pathway, and thereby ease the burden on healthcare professionals in their daily work.

Working at Siemens Healthineers can mean addressing big industry questions, such as whether there is a future for medtech without big data? “Definitely no,” says Ren-Yi Lo, Head of Siemens Healthineers Big Data Office.

So how does a Data Scientist engineer new AI models in healthcare? It all starts with the data according to Ren-Yi. From acquisition of data samples to ensuring all legal and compliance steps are fulfilled, big data when turned into smart data can fuel AI models that improve patient outcomes.

Working with smart, talented researchers

Explaining her role with Siemens Healthineers, Ren-Yi says : “I work here in tandem with 80-90 really talented, smart researchers, and it's really an honor and privilege to work with the AI scientists here. Their job is to further artificial intelligence in the healthcare space. My job is to make sure we feed the data and the appropriate types of data to enable their projects.”

Focusing on the content of the data

"I sometimes feel a bit like I'm a Jill of all trades. But I suppose that's really exactly what I have to be for my job," says Ren-Yi.

Discussing some of the challenges in her work, Ren-Yi says: “The biggest challenge I face is that the data we bring in for the projects are all very different in nature. It depends on the data source cite, depends on where it is coming from. It depends how it was acquired. My job is to make sure that we remove all the personal health information away so that we can just focus on the actual content of the data. Data management is really a very complex topic. It's very multidisciplinary. So there are aspects of the data that we need to consider from a legal basis and contract language, and we need to ensure that we disclose with our customers how the data is being used so we really work on a very transparent basis on how to acquire the data and make sure that we manage that.”

A comprehensive check in and check out process

The team has a comprehensive check in process of how to intake data, and it also has a comprehensive check out process meaning that by the time the data has been checked and validated for proper anonymization or minimization the data is kind of in the green space to use for the AI research.

“Not too long ago we released a CT scanner with a 3D camera on it,” shares Ren-Yi.

“So the whole idea with this product is that you acquire the 3D image over a camera of a person that lays on the table prior to actually scanning them so we can better position them. What you are really achieving is faster turnaround for the procedure and lower dosage on the patients,” she says.

Solving complex problems

Ren-Yi, whose parents have Chinese roots and have lived in Germany and the United States, studied computer science with a particular focus on systems engineering. "My work today as Head of the Big Data Office is substantially more varied than my university studies," notes the 40-year-old with a smile: "My job, actually, is to solve complex problems." For that to succeed, she has to speak the languages of multiple professional fields in order to coordinate various interest groups: "Medicalese" with clinical partners from whom the data originates, "Technicalese" and "Software Shop Talk" with AI research groups, developers, and her team from the Big Data Office, and "Legalese" with colleagues from the Data Protection and Privacy, Legal, or Intellectual Property Departments.