Mayo Clinic and Aiforia, a technology startup that provides deep learning AI software for image analysis in pathology, have announced a partnership to build an AI-powered pathology research support infrastructure at the Mayo Clinic. The two hope to offer speedier outcomes and scalable investigations in translational research by leveraging one other’s skills in digital pathology and deep learning.
Jukka Tapaninen, CEO of Aiforia Technologies Plc., said, “We are very excited to embark on this collaboration with Mayo Clinic. By equipping one of the world’s top-ranked hospitals with powerful deep learning-based software, leading pathologists can harness AI. This collaboration represents a significant milestone for Aiforia, in our ability to serve the growing demand for more efficient tools in digital pathology.”
Pathology is at the heart of healthcare, assisting with diagnoses, prognostics, and, of course, translational research, which brings scientific breakthroughs to the clinic. However, for the past 150 years, the same manual and subjective approaches have been used to evaluate slides. Aiforia Create, Aiforia’s deep learning AI platform, changes image-based analysis by increasing efficiency and precision beyond what is currently possible.
The Department of Laboratory Medicine and Pathology at Mayo Clinic will use Aiforia’s software in several locations across the United States. Because Aiforia’s adaptive software is cloud-based and easily integrated into Mayo Clinic’s current IT infrastructure, pathologists in different locations will be able to communicate remotely. Pathologists will be able to construct and apply AI models for image analysis in a range of research situations with the help of Aiforia Create.
Aiforia currently provides sophisticated deep learning artificial intelligence software to pathologists and scientists in preclinical and clinical labs for turning images into discoveries, judgments, and diagnoses. Aiforia’s cloud-based tools and services promise to improve the efficiency and precision of medical image analysis beyond present capabilities in a wide range of sectors, including oncology, neuroscience, and much more.