Mayo Clinic teams on AI with 3D imaging outfit

Photo: Westend61/Getty Images

Minnesota-based Mayo Clinic is joining forces with 3D imaging startup JelloX Biotech on artificial intelligence-powered imaging analysis technology.

JelloX previously participated in the fifth cohort of the Mayo Clinic and Arizona State University Alliance for Health Care MedTech Accelerator, a flagship program providing early-stage medical device and healthcare technology companies with curriculum to accelerate their go-to-market plans.

The company has developed 3D pathology imaging technology with AI for spatial analysis to support precision cancer diagnoses, with Mayo Clinic expected to further develop and validate the technology.

WHAT'S THE IMPACT

According to Mayo, cancer diagnoses using traditional 2D imaging methods can miss spatial relationships of critical tumor biomarkers and suffer from variability in the possible interpretations. 

What the new technology does is capture "enhanced spatial context," providing a 3D view of tissue architecture that hopes to improve diagnostic accuracy and consistency – and provide pathologists with a more comprehensive diagnosis for cancer patients.

The same biopsy tissue can then be analyzed by genomic sequencing and proteomics to provide patients with the full spectrum of precision medicine, the companies said.

Mayo Clinic has a financial interest in the technology and said it will use any revenue it receives "to support its not-for-profit mission in patient care, education and research."

"We are excited to bring our 3D pathology technology to research and clinical practice," said JelloX CEO and founder Dr. Yen-Yin Lin. "Our goal is to provide researchers and clinicians with more precise diagnostic tools and to support pharmaceutical companies in their quest to develop new drugs to match patients with the right treatment at the right time."

THE LARGER TREND

At the HIMSS24 global conference in Orlando, Florida, in March, a team from Mayo Clinic outlined the company's "comprehensive digital strategy," which involved several key steps, including the development of a "future state vision" and forming guiding digital imperatives.

The Mayo team has been working on this project in earnest since 2020, and throughout Phases 0 and 1 they made key decisions about the long-term master plan – a plan that includes a multiyear timeline for investing in digital experiences, with a focus on digitally optimizing its physical spaces.

Also in March, the Congressional Budget Office determined that the evidence on the usefulness of AI technology is mixed, particularly when it comes to costs.

AI and machine learning tools might affect healthcare costs in the future in many ways, the CBO said, including by detecting illness earlier or identifying patients who might benefit from preventive interventions. But while some uses of those tools might reduce costs by preventing the need for costlier care or eliminating unnecessary care, others might increase costs by spurring the development of expensive new technologies with meaningful health benefits, or by identifying additional patients who might benefit from certain medical services.

The practical application of these technologies is still inconsistent at this nascent phase – showing usefulness in predicting cancer mortality, but falling short when predicting heart failure outcomes. The CBO said it will need to see more empirical evidence before determining the overall effect on healthcare spending.

The HIMSS AI in Healthcare Forum is scheduled to take place Sept. 5-6 in Boston. Learn more and register.
 

Jeff Lagasse is editor of Healthcare Finance News.
Email: jlagasse@himss.org
Healthcare Finance News is a HIMSS Media publication.