--- title: "MONAI - Working Groups" description: "MONAI Working Groups are specialized teams that drive innovation and progress in specific areas of medical imaging AI." canonical: https://project-monai.github.io/working-groups.html audience: [engineer] last_updated: 2026-06-11 source: working-groups.html --- Community # Working Groups Small groups of researchers and engineers who own a specific area of MONAI: ultrasound, federated learning, ophthalmology, deploy, human-AI interaction. Each group meets regularly, ships code and publications, and reports to the Advisory Board. Each working group is led by chairs who serve as members of the MONAI [Advisory Board](about.html#advisory-board), which keeps each group aligned with MONAI's overall mission. Navigate ## Find Your Group Jump directly to a working group or browse through all groups below to learn about their missions and leadership. [ Deploy Research to clinical production ](#deploy)[ Developers Committee Technical excellence & code quality ](#developers)[ Education Learning resources & training ](#education)[ Evaluation & Benchmarking Guidelines & reproducibility tools ](#evaluation)[ Federated Learning Privacy-preserving collaboration ](#federated)[ Human-AI Interaction Interactive AI workflows ](#human-ai)[ Ophthalmology AI for eye disease diagnosis ](#ophthalmology)[ Outreach & Adoption Community growth & engagement ](#outreach)[ Ultrasound Scalable ultrasound AI pipelines ](#ultrasound) Our Teams ## All Working Groups From technical implementation to clinical applications, our working groups ensure that MONAI remains at the forefront of healthcare AI innovation. ### Deploy Working Group This working group aims to define how to close the existing gap from research and development to clinical production environments by bringing AI models into medical applications and clinical workflows with the end goal of helping improve patient care. The focus includes defining the open high-level functional architecture and determining which components and standard APIs are required. By collaborating with the MONAI developers, the group will move from requirements to implemented solutions. Group Leads ![Barbaros Selnur Erdal](/assets/img/people/barbaros-selnur-erdal.jpg) Barbaros Selnur Erdal Mayo Clinic ![David Bericat](/assets/img/people/david-bericat.jpg) David Bericat NVIDIA ![Haris Shuaib](/assets/img/people/haris-shuaib.jpg) Haris Shuaib Guy's and St Thomas' NHS Foundation Trust [Learn More](wg_deploy.html) ### Developers Committee Working Group This working group aims to establish and maintain technical excellence in MONAI Core by coordinating development efforts and ensuring high code quality standards. The focus includes overseeing architectural decisions, establishing development guidelines, and maintaining technical documentation with the end goal of creating a sustainable framework for medical imaging AI research and development. By fostering collaboration between contributors, the group ensures consistent implementation of best practices across the codebase. Group Leads ![Nic Ma](/assets/img/people/nic-ma.jpg) Nic Ma NVIDIA ![Eric Kerfoot](/assets/img/people/eric-kerfoot.png) Eric Kerfoot King's College London ![Yun Liu](/assets/img/people/yun-liu.jpeg) Yun Liu NVIDIA [Learn More](wg_developers.html) ### Education Working Group This working group aims to accelerate the adoption of AI in medical imaging through comprehensive educational resources and training materials. The focus includes developing structured learning paths, hands-on tutorials, and practical exercises with the end goal of enabling researchers and clinicians to effectively utilize MONAI in their work. By creating accessible content for various skill levels, the group helps bridge the knowledge gap in medical imaging AI. Group Leads ![Eric Kerfoot](/assets/img/people/eric-kerfoot.png) Eric Kerfoot King's College London ![Marc Modat](/assets/img/people/marc-modat.jpg) Marc Modat King's College London [Learn More](wg_education.html) ### Evaluation and Benchmarking Working Group The Evaluation and Benchmarking MONAI working group aims at providing guidelines, infrastructure, and practical tools for evaluation and benchmarking of medical image analysis methods. It focuses on leading the community towards the identification and adoption of best practices for evaluation and benchmarking and on identifying practical solutions to improve reproducibility. Group Leads ![Annika Reinke](/assets/img/people/annika-reinke.jpg) Annika Reinke DKFZ ![Carole Sudre](/assets/img/people/carole-sudre.jpg) Carole Sudre UCL [Learn More](wg_evaluation_benchmark.html) ### Data Quality and Federated Learning Working Group This working group aims to advance collaborative medical AI research through secure and efficient federated learning implementations. The focus includes developing standardized workflows, ensuring data compatibility, and creating modular components with the end goal of enabling distributed learning across institutions while preserving data privacy. By establishing best practices for federated learning, the group facilitates multi-institutional collaboration in medical AI research. Group Leads ![Holger Roth](/assets/img/people/holger-roth.jpg) Holger Roth NVIDIA ![Sai Praneeth Karimireddy](/assets/img/people/sai-praneeth-karimireddy.jpg) Sai Praneeth Karimireddy USC [Learn More](wg_federated_learning.html) ### Human-AI Interaction Working Group This working group aims to advance, standardize, and support human-AI interaction cycles through well-defined interfaces and community-driven development. The focus includes developing standardized APIs for AI prompting, uncertainty communication, and annotation feedback processes with the end goal of creating reusable interactive AI workflows. By fostering community-driven specification and implementation across diverse domains including radiology, pathology, and surgery, the group enables efficient AI deployment across cloud, local, and HPC environments. Group Leads ![Ralf Floca](/assets/img/people/ralf-floca.png) Dr. Ralf Floca DKFZ ![Saad Nadeem](/assets/img/people/saad-nadeem.png) Dr. Saad Nadeem Memorial Sloan Kettering Cancer Center ![Bruce Hashemian](/assets/img/people/bruce-hashemian.jpeg) Bruce Hashemian NVIDIA [Learn More](wg_human_ai_interaction.html) ### Ophthalmology Working Group This working group aims to advance the application of AI in ophthalmology through specialized tools and algorithms. The focus includes developing analysis methods for various ophthalmic imaging modalities, creating annotation tools, and building predictive models with the end goal of improving diagnosis and treatment of eye diseases. By leveraging MONAI's capabilities, the group accelerates innovation in ophthalmic image analysis. Group Leads ![Jayashree Kalpathy-Cramer](/assets/img/people/jayashree-kalpathy-cramer.jpg) Jayashree Kalpathy-Cramer University of Colorado ![Aaron Lee](/assets/img/people/lee-aaron.png) Aaron Lee University of Washington [Learn More](wg_ophthalmology.html) ### Outreach and Adoption Working Group This working group aims to expand MONAI's reach and impact across the medical imaging community. The focus includes organizing technical training, fostering community engagement, and developing infrastructure with the end goal of establishing MONAI as the leading open-source platform for medical imaging AI. By building an inclusive and transparent community, the group ensures sustainable growth and adoption of MONAI. Group Leads ![Michael Zephyr](/assets/img/people/michael-zephyr.jpg) Michael Zephyr NVIDIA [Learn More](wg_outreach_adoption.html) ### Ultrasound Working Group This working group will focus on enabling reproducible, scalable AI development for ultrasound within the MONAI ecosystem. Key priorities include harmonizing data formats (e.g., DICOM for B-mode); supporting data streaming (e.g., for RF signals); standardizing annotation formats and labeling protocols for common ultrasound tasks; and defining reusable pipelines for training, inference, evaluation, and deployment. Short-term and long-term priorities and focal clinical applications will be determined by participants of the group. By collaborating with the MONAI developers, the group will move from requirements to implemented solutions. Group Leads ![Tina Kapur](/assets/img/people/tina-kapur.jpg) Tina Kapur Brigham and Women's Hospital, Harvard Medical School ![Stephen Aylward](/assets/img/people/stephen-aylward.jpg) Stephen Aylward NVIDIA [Learn More](wg_ultrasound.html) Get Involved ## Join a Working Group Working group members collaborate regularly through meetings, workshops, and joint projects, fostering knowledge exchange and driving continuous improvement in their specialized areas. Get started by exploring the groups above or reaching out to the community. [View on GitHub](https://github.com/Project-MONAI) [Advisory Board](about.html#advisory-board)