GE Healthcare Launches New AI Innovation Lab

GE HealthCare has opened a new AI Innovation Lab to accelerate early-concept artificial intelligence (AI) solutions.

The projects are a 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 projects we’re showcasing are just some of the innovations we have underway, enabled by our AI and cloud computing capabilities. We will continue to gather feedback from our customers as we find 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 R&D efforts strive to redefine the daily experience of clinicians by creating new concepts to enhance diagnostics accuracy, reduce administrative burdens, and ensure every patient receives the most informed, personalized care possible. Examples of these concept projects follow.

Health Companion: This 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 to proactively generate treatment plan recommendations, continuously adapting based on new information. For example, GE HealthCare is exploring whether multi-agentic AI can understand the difference between an expected symptom as a function of treatment, and the same symptom as a signal of disease progression—such as cancer spread—with the goal to alert the care team as appropriate with suggested next steps. Health Companion aims to provide the collaboration and discussion similar to a multi-disciplinary care team comprised of specialized clinicians. This project is being built to incorporate safety and explainability principles.

Using AI to better predict triple negative breast cancer recurrence: GE HealthCare is supporting the Winship Cancer Institute of Emory University in research, focused on the early prediction of triple negative breast cancer recurrence. Triple negative breast cancer is the most aggressive breast cancer subtype, however, there is a shortage of tools to predict its recurrence. As many as 50% of patients currently diagnosed with early-stage triple negative breast cancer (stages I to III) experience recurrence.ⁱ This research will use deep learning to evaluate multi-modal data including genomics and pathology information to investigate whether 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; Dr. Sunil Badve from Emory University is the principal investigator (PI), and Dr. Soumya Ghose is the co-PI from GE HealthCare.

Innovating solutions to enhance care for moms and babies: Preventable risks associated with childbirth are one of the most pressing health issues currently facing women. GE HealthCare is working directly with health systems and their care teams to develop solutions to help address this challenge. For example, GE HealthCare is working on a care companion initiative that is investigating the ways in which generative AI can minimize the effort spent searching through data and seeking best practices. Powered by a large language model, this initiative intends to further explore methods to make it easy for care teams to quickly find information about standard care protocols and clinical definitions and generate patient summarizations using historical and current multi-modal data for potential use in handoffs and care transitions.

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 programming the model to automate medical report generation and interpret images into text to accelerate radiologists’ workflow, with the aim to help alleviate care teams’ administrative burdens. GE HealthCare’s research goal in this area is to provide practical value by reducing the cognitive burden to healthcare professionals seeking efficient and reliable diagnostics tools. The model is being developed as a result of GE HealthCare’s strategic collaboration with Amazon Web Services.

Helping radiologists scale mammography screenings: Approximately 90% of U.S. screening mammograms are normal, yet there is no efficient way for radiologists to quickly separate the clearly normal scans from potentially suspicious ones.ⁱⁱ GE HealthCare is developing this cloud-based AI concept to explore how foundation models can help clinicians quickly identify normal breast screening exams, allowing radiologists to focus more of their time on suspicious cases. As countries grapple with a radiologist shortage, GE HealthCare aims to work with strategic and clinical collaborators to make advances in this space to help enhance accuracy, scale screenings, and improve access to this critical type of preventive care globally.

GE HealthCare is working on AI-enabled innovations that vary in maturity and market-readiness. For example, GE HealthCare has submitted a 510(k) application with the U.S. Food and Drug Administration (FDA) requesting clearance of a new solution to address the needs of clinicians in providing care for moms and babies. This AI-powered fetal heart rate interpretation featureⁱⁱⁱ applies deep learning to waveform data to analyze fetal heart rate. This feature is designed to identify events such as accelerations and decelerations of fetal heart rates to help care teams quickly understand the baby’s health, improving what is currently a highly manual and subjective task.

These projects showcase the work underway at GE HealthCare, a company that applies a 125-year legacy of innovation with the energy of a startup as it works to help solve the healthcare industry’s most pressing challenges. GE HealthCare has been investing in AI for years and has topped an FDA list of AI-enabled device authorizations for three consecutive years with 80 authorizations.iv

References
ⁱ National Institutes of Health, “Early prediction of lethal phenotypes in triple negative breast cancer using multiscale, multi-modality platforms,” https://reporter.nih.gov/project-details/10883284.
ⁱⁱ “Breast Cancer Screening (PDQ®)–Health Professional Version,” March 28, 2024,  https://www.cancer.gov/types/breast/hp/breast-screening-pdq.
ⁱⁱⁱ The FHR AI 510(k) has been submitted to the FDA and is not currently available for sale in the United States.
^iv U.S. Food and Drug Administration, “Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices,” August 7, 2024, https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices.