Industry Day

Jona

Monday | April 14, 2025 | 8:40 – 10:00

Abstract: Artificial intelligence (AI) has immense potential to transform clinical practice by enabling precision medicine, optimizing decision-making and offering large-scale personalization of patient care. However, translating AI from research to real-world clinical applications presents significant challenges, including integration in clinical pathways, regulatory, messaging and appropriate payment structures. In this talk, we discuss key strategies to overcome these barriers to help researchers, clinicians and industry leaders bridge the gap between AI innovation and clinical implementation. As a case study, we will examine how Jona has created a custom LLM and digital twin to analyze complex gut microbiome data and assess interventions. We show how we have been able to apply these strategies to bring powerful new AI technology into clinical practice.

Biography: Leo is internationally recognized for his work to deliver AI in health for over 20 years at pioneering bay area startups (HeartFlow), multinational medical companies (Siemens) and as CEO of Paige.ai. As CEO of Paige, Leo led the company to become an industry leader, internationally launching groundbreaking products and receiving the first-ever FDA approval for an AI product in pathology. Leo’s new company, Jona, has developed a unique AI and digital twin to both decode and shape the gut microbiome to significantly improve health. Leo has authored two books on AI, over 100 peer-reviewed scientific papers and is an inventor on over 300 patents. Winner of the Edison Patent Award, he was inducted as Fellow in the American Institute for Medical and Biological Engineering. Leo earned a Ph.D. in Cognitive and Neural Systems from BU. Leo is CEO in Residence with Breyer Capital and the Founder and CEO of Jona.

Google Health

Monday | April 14, 2025 | 10:30 – 11:15

Abstract: The potential of artificial intelligence (AI) in healthcare has been limited by the breadth of the applications required for transformative impact and high cost of building task-specific models, due to data and compute requirements. Recent work unlocks the potential of new capabilities and applications including zero-shot classification, multimodal classification, semantic search, visual question answering, and automated radiology report generation. To accelerate the adoption of AI in medicine, we created Health AI Developer Foundations (HAI-DEF), a suite of foundational models spanning diverse medical modalities, including computed tomography, pathology, dermatology, chest X-ray, and bioacoustic data. HAI-DEF models, trained on large datasets, can significantly reduce the data and computational resources required for downstream AI development. These models can be used to advance novel research investigations and medical device development by setting a strong baseline in performance that can be further improved upon by researchers and developers. This work provides a critical step towards more accessible, powerful, and versatile AI solutions in healthcare.

Biography: Atilla has over 20 years of experience in healthcare research and innovation. As part of Health AI at Google Research, he developed state-of-the-art lung and breast cancer detection systems, from conception to medical devices and technologies deployed internationally through healthcare partnerships. Currently, his focus is on creating generalizable foundational models to accelerate healthcare discovery and innovation. Prior to Google, Atilla contributed to startups and medical imaging companies, developing novel technologies for radiology and interventional radiology, earning an R&D 100 Award. He has authored over 100 publications and patents and co-authored an ISO standard on AI bias. He holds PhD and MS degrees in Computer Science and Electrical Engineering, specializing in medical imaging, from The Pennsylvania State University. Atilla is passionate about AI’s potential to revolutionize healthcare.

GE HealthCare

Monday | April 14, 2025 | 11:15 – 12:00

Abstract: The rapid advancement of AI is revolutionizing healthcare by enhancing the efficiency and quality of model development. Foundation models pretrained on diverse medical imaging and text data are driving this transformation by enhancing clinical performance, reducing dependency on extensive labeled datasets, and enabling faster downstream application development. Moreover, innovative approaches that leverage the pretrained models with text prompts and direct preference optimization empower clinical experts to guide model improvements with minimal input, significantly alleviating the annotation burden. Through real-world examples and the latest research, this talk will explore how these advancements are reshaping healthcare AI—creating more efficient development pipelines and enabling scalable, intelligent clinical applications. Additionally, this talk will discuss the challenges in healthcare AI and explore the opportunities foundation models present in maximizing the value of clinical data and advancing personalized healthcare solutions.

Biography: Dr. Zhijian Yang is an AI research scientist at GE Healthcare, leading the development of MRI foundation models with a focus on improving efficiency in model development pipelines. He earned his PhD in Applied Mathematics and Computational Science from the University of Pennsylvania. Dr. Yang is dedicated to bridging the gap between AI advancements and clinical needs. His research has been focusing on how deep learning can advance personalized medicine by integrating MRI, genetics, and investigating the heterogeneity of neurological diseases. His work has been published in top journals and conferences, including Nature Medicine, Nature Communications, ICLR, and CVPR, and has been featured by Nature News.

Subtle Medical

Monday | April 14, 2025 | 13:30 – 14:15

Abstract: The rapid evolution of artificial intelligence and generative AI is transforming clinical imaging, offering unprecedented opportunities to enhance diagnostic workflows. In this talk, we present our latest advancements in AI-powered imaging applications, focusing on augmenting radiology data generation: acceleration, enhancement, synthesis, and low-dose imaging. We will discuss how deep learning and generative models are integrated into our imaging platforms to significantly reduce scan times while improving image quality and diagnostic accuracy. Our approach leverages AI to standardize imaging protocols and synthesize missing contrast sequences, enabling superior visualization of anatomical structures and pathology. Furthermore, by optimizing imaging techniques, we have achieved ultra-low-dose imaging solutions that maintain diagnostic integrity while minimizing risks. Clinical validation studies across multiple institutions and clinical adoption in hundreds of sites globally have demonstrated the clinical and operation value and potential for widespread adoption of these technologies, marking a pivotal step towards next-generation medical imaging and improved patient care.

Biography: Dr. Enhao Gong is a researcher and entrepreneur specializing in the application of AI/GenAI in medical imaging. Dr. Gong holds MS and PhD in Electrical Engineering from Stanford University, focusing on deep learning applications for medical image acquisition, reconstruction, enhancement, and quantification.

As the founder and CEO of Subtle Medical, he leads the development of cutting-edge AI technologies that enhance medical imaging acquisition, reconstruction, and quantification. Under his leadership, Subtle Medical has achieved significant milestones, including securing 9 FDA clearances for its AI-powered products and servicing over 700 hospitals and imaging centers worldwide. The company has raised over $50 million from top venture capitalists and has been recognized with prestigious awards such as the NVIDIA Inception AI Award, CB Insights AI-100, Top 50 GenAI, etc. Dr. Gong’s groundbreaking work has been awarded by RSNA, Forbes 30-under-30, Radiology Business 40-under-40, Fortune 40-under-40, and Top 50 Healthcare AI Entrepreneur Award.

United Imaging Intelligence

Monday | April 14, 2025 | 14:15 – 15:30

Abstract: Artificial intelligence (AI) is transforming medical imaging by addressing critical clinical challenges across various applications. Recently, foundation models have emerged as a powerful solution, offering a scalable learning framework that enhances model generalization, enables few-shot learning, and improves real-time imaging applications. In this talk, we will also explore the role of diffusion models as prior distribution foundation models, particularly in accelerating Cardiac MRI (CMR) acquisitions to enhance image quality and ensure more reliable visualization. In interventional cardiology, low-dose imaging often compromises device and vessel visibility, impacting procedural accuracy. To address this, we introduce a contrastive learning-based approach that improves vessel visualization in real-time imaging, aiding precise device placement. Additionally, we present our work on label-efficient data augmentation using video diffusion models for guidewire segmentation, demonstrating how diffusion-based foundation models optimize downstream tasks.

By integrating diffusion models, contrastive learning, and few-shot adaptation, foundation models are driving advancements in AI-driven cardiac imaging. This talk will showcase their impact on CMR, interventional imaging, and real-time decision support.

Biography: Dr. Shanhui Sun is the Senior Director of AI and Medical Imaging at UII America Inc., based in Boston, MA, a subsidiary of Shanghai United Imaging Intelligence Healthcare Co. Ltd. (UII). His research focuses on medical image and video analytics, machine learning, cardiac MRI, breast imaging analysis, and AI-driven imaging, with expertise in MRI reconstruction and image enhancement. His recent work explores generative AI and diffusion models for image restoration, denoising, and data augmentation. He also specializes in AI-driven interventional imaging, including real-time imaging enhancements, device tracking, and surgical navigation optimization. Dr. Sun is actively involved in deploying AI models into clinical workflows, ensuring seamless integration and scalability. He has co-authored over 50 publications and co-invented more than 80 granted patents. Previously, he was a Principal Scientist at CuraCloud and a Staff Scientist at Siemens. He earned his Ph.D. from the University of Iowa in 2012.

NVIDIA

Monday | April 14, 2025 | 15:30 – 16:15

Abstract: From the hospital-scale to the molecular-scale, simulations will drive future MedTech AI R&D. This talk will provide a high-level overview of how AI will enhance MedTech simulations and how physical and physiological simulations will enhance MedTech AI. The enabling platforms for and future products from these simulations will be explored. In particular, the talk will describe the evolving role of the open-source MONAI platform, the capabilities of Omniverse for simulation, and the optimizations offered by the open-source HoloscanSDK for medical AI. Opportunities for algorithm researchers, data scientists, medical professionals, and entrepreneurs to build upon and contribute back to these platforms will be detailed.

Biography: Dr. Stephen Aylward is the Global Lead for Developer Relations for Medical Technologies at NVIDIA. Dr. Aylward has over 25 years of experience as PI on over $36M in NIH, DARPA, and DoD-funded medical imaging research projects, and he has served as the leader of numerous open-source software platforms, e.g., ITK, VTK, 3D Slicer, and MONAI. At NVIDIA, he is combining open-source software with accelerated computing to explore and support next-gen, AI-enhanced, clinical products. His interests range from intelligent, point-of-care ultrasound devices to neuro-interventional systems. He is also a MICCAI Fellow, an adjunct professor in Computer Science at UNC, an advisory board member for multiple academic and commercial organizations, and chair of the MONAI Advisory Board. Previously, he was Senior Director of Strategic Initiatives at Kitware, and before founding Kitware’s office in North Carolina, he was a tenured professor in Radiology at UNC.