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Minnesota-based Mayo Clinic is teaming up with 3D imaging startup JelloX Biotech to develop AI-powered image analysis technology.
JelloX previously participated in the fifth edition of the Mayo Clinic and Arizona State University Alliance for Health Care MedTech Accelerator, a flagship program that provides early-stage medical technology and medical device companies with a curriculum designed to accelerate their go-to-market plans.
The company has developed 3D pathology imaging technology using AI for spatial analysis to aid in precise cancer diagnosis. Mayo Clinic is expected to further develop and validate the technology.
WHAT IS THE IMPACT
According to Mayo, diagnosing cancer with established 2D imaging methods may not take into account the spatial relationships of key cancer biomarkers, and possible interpretations can be variable.
The modern technology captures “extended spatial context,” providing a three-dimensional view of tissue architecture that is expected to improve diagnostic accuracy and consistency, as well as provide pathologists with a more comprehensive diagnosis of cancer patients.
The companies said the same biopsy tissue can then be analyzed for genome sequencing and proteomics, allowing patients to benefit from the full spectrum of precision medicine.
Mayo Clinic has a financial interest in the technology and has said it will employ any revenue generated “to support its nonprofit mission of patient care, education and research.”
“We are excited to bring our 3D pathology technology to clinical research and practice,” said JelloX CEO and Founder Dr. Yen-Yin Lin. “Our goal is to provide researchers and clinicians with more precise diagnostic tools and support pharmaceutical companies in their quest to develop new medicines to match patients with the right treatment at the right time.”
BIGGER TREND
At the HIMSS24 global conference in Orlando, Florida, in March, the Mayo Clinic team presented “comprehensive digital strategy”, which included several key steps, including developing a “vision of the future state” and formulating guiding digital imperatives.
The Mayo team has been working intensively on this project since 2020, and in Phases 0 and 1, they made key decisions about the long-term master plan – a plan that includes a multi-year roadmap for investments in digital experiences, with a focus on digitally optimizing physical spaces.
Also in March, the Congressional Budget Office found that the evidence for the usefulness of AI technologies is mixedespecially when it comes to costs.
The CBO said AI and machine learning tools could impact health care costs in the future in a number of ways, including detecting diseases earlier or identifying patients who could benefit from preventive interventions. But while some applications of these tools could lower costs by preventing the need for more high-priced care or eliminating unnecessary care, others could boost costs by spurring the development of high-priced modern technologies with significant health benefits or identifying additional patients who could benefit from certain health services.
The practical application of these technologies remains inconsistent in this early phase—showing utility in predicting cancer mortality but failing to meet expectations in predicting heart failure outcomes. CBO said it would need to see more empirical evidence before determining the overall impact on health care spending.
is the editor of Healthcare Finance News.
E-mail address: jlagasse@himss.org
Healthcare finance news is HIMSS Media publication.