GE HealthCare Announces AI Innovation Lab Initiative

GE HealthCare announced a new AI Innovation Lab, an initiative designed to accelerate early-concept AI innovations within the company. These projects are one part of GE HealthCare’s broader AI and digital strategy, which is focused on integrating AI into medical devices, building AI applications that enhance decision-making across the care journey and disease states, and using AI to support better outcomes and operational efficiencies system wide. The company’s investment in cloud technology underpins this strategy, providing the computing power to drive the development of AI at scale.

"The AI Innovation Lab lifts the curtain on the work we are undertaking at the vanguard of healthcare innovation. At GE HealthCare, we're not just developing technology—we're striving to break new ground by exploring novel ways that AI could enable healthcare. For example, through projects like Health Companion, we are evaluating ways to apply agentic AI in order to bring the clinical knowledge and problem-solving insights of a multi-disciplinary medical team to clinicians’ fingertips and help them take action,” said Dr Taha Kass-Hout, GE HealthCare's Global Chief Science and Technology Officer. “The pioneering projects we’re showcasing today are just some of the projects we have underway, enabled by our AI and cloud computing capabilities. We will continue to gather feedback from our customers as we innovate ways to help them apply AI to their health data and convert information into actionable, care-enhancing strategies.”

GE HealthCare’s AI and cloud-related research and development efforts are focused on redefining the day-to-day experience of clinicians by creating new concepts to enhance the accuracy of diagnostics, reduce administrative burdens, and ensure that every patient receives the most informed, personalized care possible. Examples of these concept projects: 

• Bringing the knowledge of a multi-disciplinary team to clinicians’ fingertips with agentic AI: Health Companion project explores whether an agentic AI approach driven by multiple agents, each an expert in a particular area (i.e., genomics, radiology, pathology, etc.), could help physicians streamline their clinical decision-making and deliver more personalized care. The project’s vision is for these agents to collaborate and analyze multi-modal data in order to proactively generate treatment plan recommendations, continuously adapting based on new information.
• Helping radiologists scale mammography screenings: Currently, approximately 90% of screening mammograms in the U.S. are normal, yet there is no efficient way for radiologists to quickly separate the clearly normal scans from potentially suspicious ones.i GE HealthCare is developing this cloud-based AI concept to explore how foundation models can help clinicians quickly identify normal breast screening exams, to help radiologists focus more of their time on suspicious cases.
• Using AI to better predict triple negative breast cancer recurrence: GE HealthCare is supporting the Winship Cancer Institute of Emory University on research focused on the early prediction of triple negative breast cancer recurrence. The goal of this research is to use deep learning to evaluate multi-modal data including genomics and pathology information to investigate if AI can better predict the likelihood of recurrence, and help the care team inform a treatment plan and monitoring schedule. This research is being funded by a grant from the National Institutes of Health (NIH Grant# 1R01CA281932-01A1).
• Innovating solutions to enhance care for moms and babies: Preventable risks associated with childbirth are one of the most pressing health issues facing women today. GE HealthCare is working directly with health systems and their care teams to develop solutions that help address this challenge, including a care companion initiative that is investigating how generative AI could minimize the effort spent searching through data and seeking best practices.
• Researching multi-modal X-Ray foundation model: GE HealthCare is working on a research project to create a full-body foundation model, built on a dataset of 1.2 million anonymized PHI-free X-ray images from diverse regions across the body. This model shows great potential, and is yielding promising early internal benchmark testing on key tasks including segmentation, classification, and visual localization. The project is also experimenting with having the model automate medical report generation and interpreting image into text to accelerate the workflow for radiologists, with the aim to help alleviate care teams’ administrative burdens.