Working Groups

MONAI Working Groups are specialized teams that drive innovation and progress in specific areas of medical imaging AI. Each group brings together experts from diverse backgrounds to address key challenges and opportunities in their respective domains.

These groups focus on developing solutions, establishing standards, and creating resources that benefit the entire medical imaging AI community. From technical implementation to clinical applications, our working groups ensure that MONAI remains at the forefront of healthcare AI innovation.

Working group members collaborate regularly through meetings, workshops, and joint projects, fostering knowledge exchange and driving continuous improvement in their specialized areas.

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Deploy Developers Committee Education Evaluation and Benchmarking Federated Learning Human-AI Interaction Ophthalmology Outreach and Adoption

Deploy Working Group

MISSION

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

    Barbaros Selnur Erdal

    Mayo Clinic

  • David Bericat

    David Bericat

    NVIDIA

  • Haris Shuaib

    Haris Shuaib

    Guy's and St Thomas' NHS Foundation Trust

Developers Committee Working Group

MISSION

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 robust and 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

    Nic Ma

    NVIDIA

  • Eric Kerfoot

    Eric Kerfoot

    King's College London

  • Yun Liu

    Yun Liu

    NVIDIA

Education Working Group

MISSION

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

    Eric Kerfoot

    King's College London

  • Marc Modat

    Marc Modat

    King's College London

Evaluation and Benchmarking Working Group

MISSION

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

    Annika Reinke

    DKFZ

  • Carole Sudre

    Carole Sudre

    UCL

Data Quality and Federated Learning Working Group

MISSION

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

    Holger Roth

    NVIDIA

  • Sai Praneeth Karimireddy

    Sai Praneeth Karimireddy

    USC

Human-AI Interaction Working Group

MISSION

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 robust, 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

    Dr. Ralf Floca

    DKFZ

  • Saad Nadeem

    Dr. Saad Nadeem

    Memorial Sloan Kettering Cancer Center

  • Bruce Hashemian

    Bruce Hashemian

    NVIDIA

Ophthalmology Working Group

MISSION

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

    Jayashree Kalpathy-Cramer

    University of Colorado

  • Aaron Lee

    Aaron Lee

    University of Washington

Outreach and Adoption Working Group

MISSION

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

    Michael Zephyr

    NVIDIA