CM4AI Graph Community Detection Challenge
2025-05-14 - 2025-07-31
The Opportunity and A Call to Action
We are excited to announce the launch of the CM4AI Graph Community Detection competition on Kaggle! Participating in this challenge will give you a unique opportunity to be part of groundbreaking advancements in biomedical research as part of the Cell Maps for AI (CM4AI) initiative.
Challenge Dates: May 14, 2025 – July 31, 2025
Join the Frontier of Biomedical AI Research!
The Bridge2AI Functional Genomics Grand Challenge (Cell Maps for AI/CM4AI) is pleased to announce our Kaggle competition focused on using the data and tools generated by CM4AI and leveraging emerging AI/ML methods, such as graph and quantum machine learning, to advance biomedical science and precision medicine.
Competition Overview
The goal of this competition is to develop methods that identify communities within biological networks to uncover hidden structures and provide new insights into biological systems. By participating, you will help push the boundaries of AI/ ML applications in the life sciences.
Why Participate?

Shape the Future of Science: Successful approaches can redefine how we understand cellular systems, paving the way for innovative therapeutic strategies for cancer and other human disease.
Challenge Yourself: Engage in solving a cutting-edge problem that bridges the gap between AI/ML and molecular biology.
Be Part of a Global Community: Collaborate and compete with experts, researchers, and enthusiasts from diverse fields with mentorship from CM4AI investigators.

Key Details

CM4AI Resources: https://cm4ai.org and https://youtube.com/@CM4AI
Competition Link: https://www.kaggle.com/t/b25c9b18a199411892011bfb88680cf3
Objective: Detect and identify communities from CM4AI SEC-MS data, contributing to the Cell Maps for AI initiative.

Who Should Join?
This competition is open to anyone passionate about artificial intelligence/machine learning, computational biology, or biomedical research. Whether you’re a seasoned expert or an enthusiastic beginner, your contributions can help drive the next wave of discoveries.
Don’t miss this opportunity to be part of a transformative journey at the intersection of AI and molecular biology!

Join the Challenge Now

Spring 2025 All Hands & Open House Meeting
2025-05-20 - 2025-05-22 | NIH Neuroscience Center, 6001 Executive Blvd, Rockville, MD, 20852, United States
All Hands Conference
Our May 2025 All Hands Conference will be an opportunity for researchers and experts to engage in vibrant discussions. The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2Al) program will propel biomedical research forward by setting the stage for widespread adoption of artificial intelligence (Al) that tackles complex biomedical challenges beyond human intuition. The biomedical research community generates a wealth of data, but most of these data are not suitable for machine learning because they are incomplete. By bringing technological and biomedical experts together with social scientists and humanists, the Bridge2Al program will help bring solutions to this deficit.
Open House
The Bridge2Al Open House will focus on collaborative approaches, learn about current considerations, explore new datasets, and discuss other key issues related to bridging the gap from biomedical information to Al. Sessions will be centered around the activities of the Bridge2Al Data Generation Projects, which stemmed from Grand Challenges put forth by the National Institutes of Health (NIH).
📋 Check out Digital Program

Bridge2AI Spring 2025 Open House
2025-05-22 | NIH Neuroscience Center 6001 Executive Blvd Rockville MD United States
Join us for the second annual Bridge2AI Open House on Thursday, May 22, 2025, in Rockville, MD! This event provides an amazing opportunity for scientists, ethicists, coders, community members, and practitioners with diverse expertise and experience levels to come together to build unique solutions that will help solve a range of relevant problems. The Bridge2AI Open House will focus on key issues related to bridging the gap from biomedical information to AI. Sessions will be centered around the activities of the Bridge2AI Data Generation Projects, which stem from the National Institutes of Health’s (NIH) Grand Challenges.
Register By: May 16, 2025
Register Here

B2AI Discussion Forum on Emerging ELSI Issues: “Ethical, Legal, and Social Implications in AI Quality Assurance and Model Validation: Case Studies and Evolving Approaches” by Shannon McWeeney, Ph.D.
2025-06-17 15:00 - 16:00 (America/New_York)

MICCAI 2025 Multi Camera Robust Diagnosis of Fundus Diseases(MuCaRD) Challenge
2025-06-30 - 2025-10-10
Introduction
Fundus imaging is an indispensable tool in primary care for the early detection of major ophthalmic diseases—such as diabetic retinopathy and glaucoma—and for guiding treatment decisions. By noninvasively visualizing the retinal vasculature and subtle changes at the optic nerve head, fundus exams also serve as indicators of systemic health, making them a first line of patient management. With the widespread adoption of high-resolution, digital camera–based fundus imaging, a variety of imaging modalities have rapidly entered clinical practice. Recently, deep-learning–based models for classifying fundus diseases have demonstrated high sensitivity and specificity and have proven their clinical utility by being integrated into numerous software medical devices (SaMD). For example, automated diabetic retinopathy screening systems and glaucoma-progression monitoring tools are already commercially available, contributing broadly to diagnostic support and patient screening. However, most models are trained and validated on data from a single camera type, which limits their performance when applied to images from new or infrequently used devices. To overcome these practical constraints, this challenge aims to develop AI models that deliver consistent diagnostic performance across diverse camera environments. Through the Multi-Camera Robust Diagnosis of Fundus Diseases (MuCaRD) challenge, we will evaluate both robust classification algorithms that generalize to unseen devices and adaptive learning techniques that can quickly fine-tune using only a few sample images from a new camera.

Challenge Description

Overview:
The MuCaRD challenge addresses a critical gap in AI‐driven fundus screening: ensuring consistent performance across both familiar and unseen camera systems. Participants will develop and benchmark models under realistic constraints—training on a limited set of images from one device and then evaluating robustness and adaptability on entirely new devices. By simulating clinical and commercial deployment scenarios, MuCaRD promotes methods that generalize beyond a single data source and can quickly fine‐tune to novel imaging hardware.

Tasks:

Task 1: Zero-Shot Classification
Train on fundus images from a single camera and evaluate on completely unseen devices. Participants perform two separate binary classifiers (glaucoma vs. normal, and referable DR vs. non-referable), submitting full model code and weights to the CodaLab platform. A hidden validation set (200 images each from Optomed Aurora, Mediworks FC162, Optos Ultra Wide, Canon CR2) and a similarly‐sized test set ensure no data leakage.
Task 2: Few-Shot Test-Time Adaptation
Extend Task 1 by leveraging a small support set (5 labeled images per new camera: 1 positive, 4 negative) provided online during validation and test phases. Models should demonstrate on-the-fly adaptation within a 10 s/image inference limit, showcasing both robustness and efficient fine-tuning.

Datasets:

AI-READI dataset: A rigorously curated set of high-resolution color fundus images acquired on Optomed Aurora and Eidon cameras across three Bridge2AI partner sites (UAB, UCSD, UW). Images span all four type 2 diabetes severity categories and include expert-verified annotations for diabetic retinopathy stage, image quality scores, and linked clinical metadata (age, sex, HbA1c, blood pressure, comorbidities). This cohort is optimized to benchmark zero-shot model performance and cross-device generalization.
Mediwhale Collection: Training from one CR2 and testing images from 5 different cameras.

Evaluation & Metrics:
Performance is measured by the average of the Area Under the ROC Curve (AUROC) and the Area Under the Precision–Recall Curve (AUPRC) for each disease. To mirror clinical feasibility, all inference and adaptation steps must complete within 10 seconds per image, though this limit does not directly penalize the score. Submissions are limited to two runs per day during validation to curtail leaderboard overfitting.

Important Dates

Training Release: June 30, 2025
Validation Submission: June 30 – August 15, 2025
Test Submission: August 15 – August 23, 2025
Winner Announcement: August 30, 2025
Workshop: October 6–10, 2025

Awards
Certificates will be presented to the top three teams in each task.
The first and corresponding authors of the winning teams will be invited to co-author the challenge summary paper and to present their results at the workshop.

Contact
For inquiries, please email: g.young@mediwhale.com

B2AI Discussion Forum on Emerging ELSI Issues: “The Pulse of Ethical Machine Learning in Health” by Marzyeh Ghassemi, Ph.D.
2025-07-15 15:00 - 16:00 (America/New_York)
Please join us on Tuesday, July 15th, 2025 at 12pm-1pm PST/3pm-4pm EST for the discussion forum:
“The Pulse of Ethical Machine Learning in Health“, by Dr. Marzyeh Ghassemi
Registration not required!
Additional details in the attached documents and message below.
Bio:
Dr. Marzyeh Ghassemi is an Associate Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES). She holds MIT affiliations with the Jameel Clinic, LIDS, IDSS, and CSAIL. For examples of short- and long-form talks Professor Ghassemi has given, see her Forbes lightning talk, and her ICML keynote.
Professor Ghassemi holds a Germeshausen Career Development Professorship, and was named a CIFAR Azrieli Global Scholar and one of MIT Tech Review’s 35 Innovators Under 35. In 2024, she received an NSF CAREER award, and Google Research Scholar Award. Prior to her PhD in Computer Science at MIT, she received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University.
Professor Ghassemi’s work spans computer science and clinical venues, including NeurIPS, KDD, AAAI, MLHC, JAMIA, JMIR, JMLR, AMIA-CRI, Nature Medicine, Nature Translational Psychiatry, and Critical Care. Her work has been featured in popular press such as MIT News, The Boston Globe, and The Huffington Post.

Cell Maps for AI (CM4AI) CodeFest at University of Alabama Birmingham
2025-08-14 08:00 - 2025-08-15 17:00 (America/Los_Angeles) | University of Alabama at Birmingham
Are you interested in AI/ML and their applications in biomedicine and precision healthcare? Join the Functional Genomics Grand Challenge team and their group of interdisciplinary experts for the 2025 Cell Maps for AI (CM4AI) CodeFest at UAB.
This two-day, in-person event is a hands-on workshop centered on the development of team-based projects using CM4AI datasets and state-of-the-art AI/ML tools to design and test novel approaches in cancer research, drug discovery, systems biology, and precision medicine. Teams will present their projects virtually, with cash awards available for the top 3!
To register, please visit the link here.
Registration Deadline: August 6th, 2025
For questions, please contact: cm4ai@yale.edu
CodeFest Themes
Participants will collaborate in teams to build and present projects that leverage CM4AI data and tools, with guidance from mentors at UAB, Yale, UCSD, and other CM4AI institutions.
Focus areas include:

Biomedical graph networks
Graph & quantum AI/ML methods
Data embedding
Data visualization
Visible neural networks
Large language models

B2AI Discussion Forum on Emerging ELSI Issues: “Data Management in the Extremes” by Dr. Jennifer Wagner
2025-08-19 15:00 - 16:00 (America/New_York)
Please join us on Tuesday, August 19th, 2025 at 12pm-1pm PST/3pm-4pm EST for the discussion forum: “Data Management in the Extremes”, by Dr. Jennifer Wagner.
Registration not required!
Additional details in the attached documents and message below.
Bio:
Jennifer K. Wagner, JD, PhD, is an Assistant Professor at Penn State University, affiliated with law, engineering, anthropology, and biomedical sciences. A licensed attorney since 2007, her interdisciplinary research focuses on the human right to science, privacy, and equity in genetic and digital health technologies. She has published over 70 articles, received NIH funding, and her work has been cited by the U.S. Supreme Court. She serves on editorial boards and national ethics committees and teaches courses on genetics law, privacy, and biomedical AI.

B2AI Discussion Forum on Emerging ELSI Issues: “Rational Health AI and Respect for Persons” by Dr. Ida Sim
2025-09-16 15:00 - 16:00 (America/New_York)
Please join us on Tuesday, September 16th, 2025 at 12pm-1pm PST/3pm-4pm EST for the discussion forum: “Relational Health AI and Respect for Persons”, by Dr. Ida Sim
Registration not required!
Additional details in the attached documents and message below.
Bio:
Ida Sim, MD, PhD is Professor of Medicine (UCSF) and Computational Precision Health (UCSF and UC Berkeley) and Co-Director of the UCSF UC Berkeley Joint Program in Computational Precision Health. She obtained her B.Sc. in Biology, her MD, and her PhD in Medical Informatics from Stanford. A practicing primary care physician, Dr. Sim completed her Internal Medicine internship and residency at the Massachusetts General Hospital and a fellowship in General Medicine at the Palo Alto VA.

Dr. Sim’s research is on cyberinfrastructure and policies for large-scale health data sharing and AI for managing multiple chronic conditions in primary care. She is Co-founder of Open mHealth, which defines the IEEE 1752 global open standard for patient-generated health data interoperability, and co-leads JupyterHealth, a new project bringing the Jupyter ecosystem to healthcare. She is also Co-founder of Vivli, the world’s largest platform for clinical trial data sharing. In prior work, Dr. Sim was the founding Project Coordinator of the World Health Organization’s International Clinical Trials Registry Platform and led the establishment of the first global policy on clinical trial registration.

Dr. Sim is a member of the National Academy of Medicine and the American Society for Clinical Investigation, a Fellow of the American College of Medical Informatics, and a recipient of the United States Presidential Early Career Award for Scientists and Engineers (PECASE).

B2AI Discussion Forum on Emerging ELSI Issues: “The Observer Project: Transforming Healthcare from the Outside” by Dr. Kevin Johnson
2025-10-21 15:00 - 16:00 (America/New_York)
Please join us on Tuesday, October 21st, 2025 at 12pm-1pm PST/3pm-4pm EST for the discussion forum: “The Observer Project: Transforming Healthcare from the Outside”, by Dr. Kevin Johnson.
Registration not required!
Additional details in the attached documents and message below.
Bio:
Kevin B. Johnson, MD, MS is the David L. Cohen University Professor of Biomedical Informatics, Computer Science, Pediatrics, and Science Communication at the University of Pennsylvania, and Vice President of Applied Clinical Informatics in the University of Pennsylvania Health System. He received his MD from Johns Hopkins and his MS in Medical Informatics from Stanford University. Previously, he served as Chair for the Department of Biomedical Informatics and Chief Informatics Officer for Vanderbilt University Medical Center.

A clinical informatics researcher, Johnson was among the world’s first researchers to propose and demonstrate the value of text-messaging in behavior change. Dr. Johnson developed the first ever computer-based documentation system used at Hopkins, as well as the first e-prescribing system used at Vanderbilt—both of which became platforms for numerous evaluations by Johnson and his trainees. As director of the Penn Artificial Intelligence for Ambulatory Care Innovation (AI4AI), he and his lab focus on various uses of AI to reimagine the clinical encounter.

He has authored over 200 publications and has won numerous national awards. He was elected to the American College of Medical Informatics in 2004 (FACMI) and (FAMIA), The Academic Pediatric Society in 2010, the National Academy of Medicine in 2010, the International Association of Health Science Informatics in 2021 (FIAHSI), the American Institute of Medical and Biological Engineering in 2022 (FAIMBE).

TRM LLM Module Lecture: "Spatial Intelligence: A Key to Embodied AI" with Parisa Kordjamshidi
2025-12-11 12:00 - 13:00 (America/Los_Angeles)
Join us on Thursday, December 11, 2025, at 12:00 PM PT/3:00 PM ET for the lecture: “Spatial Intelligence: A Key to Embodied AI” with Parisa Kordjamshidi, PhD. This will be the 5th installment in the Bridge2AI Training, Recruitment, and Mentoring (TRM) 2025-26 Lecture Series Large Language Model (LLM) Module.
Learning Objectives:

Define spatial reasoning and generalization within the context of AI model development.
Identify and explain the current limitations of large vision–language models.
Describe neurosymbolic AI modeling and articulate how it can enhance model generalization

Biography:
Dr. Parisa Kordjamshidi is an Associate Professor of Computer Science and Engineering at Michigan State University since 2019. After her PhD (KU Leuven, Belgium), she completed her postdoctoral training at the KnowEng Center of NIH BD2K Program at UIUC. At MSU, her research spans Artificial Intelligence (AI), machine learning, natural language processing (NLP), neuro-symbolic AI, and multi-modal LLMs. She currently directs the Heterogeneous Learning & Reasoning Lab at MSU. She is the recipient of the NSF CAREER Award (2019–24), the Amazon Faculty Research Award (2022), and the Fulbright Scholar Award (2025).
Dr. Kordjamshidi has a strong publication record across leading AI and NLP venues, including ACL, EMNLP, NAACL, NeurIPS, AAAI, IJCAI, and ICLR. She serves as the Action Editor of TACL and as a member of the editorial boards of JAIR and other impactful journals. She has served on organizing committees for major conferences such as NAACL, EMNLP, ECML-PKDD, and AAAI, and as a (senior) area chair or program committee member for top venues including ACL, IJCAI, NAACL, EMNLP, and AAAI.

TRM LLM Module Lecture: “Navigating Ethical AI in Healthcare: Protecting Consumers and Patients” with Adela Grand
2025-12-18 12:00 - 13:00 (America/Los_Angeles)
Join us on Thursday, December 18, 2025, at 12:00 PM PT/3:00 PM ET for the lecture: “Navigating Ethical AI in Healthcare: Protecting Consumers and Patients” with Adela Grand, PhD. This will be the 6th installment in the Bridge2AI Training, Recruitment, and Mentoring (TRM) 2025-26 Lecture Series Large Language Model (LLM) Module.
Learning Objectives:

Explain why ethical principles are essential in the design and evaluation of AI-based mental health applications.
Identify and critique key components of the FUTURE-AI framework for ensuring trustworthy and human-centered AI systems.
Apply the FUTURE-AI framework to evaluate an AI-based mental mobile app for robustness.

Biography:
Dr. Adela Grando, PhD, FAMIA, FACMI, is a Professor of Biomedical Informatics at Arizona State University and an Adjunct Assistant Professor at the Mayo Clinic. At ASU, her research focuses on healthcare information technology, patient-centered technologies, and mobile health, with an emphasis on privacy-preserving data sharing and clinical decision support. She completed postdoctoral training at UCSD’s Division of Biomedical Informatics and at Oxford and Edinburgh Universities through UK Cancer Research, following her PhD training in Spain.
Dr. Grando has an extensive publication record in leading venues, including AI in Medicine, the Int. J. of Med. Inform., and Applied Clin. Inform. She previously served as Associate Editor for the Health Informatics Journal and currently serves on the editorial boards of the Journal of Biomedical Informatics and AI in Medicine. She is recognized as a Fellow of both the American Medical Informatics Association (FAMIA) and the American College of Medical Informatics (FACMI).

Bridge2AI Discussion Forum on Emerging ELSI Issues: “Trust Starts Upstream: Recommendations for Creating Ethically Sourced Health Data Repositories for AI/ML"
2026-01-20 15:00 - 16:00 (America/New_York)
Please join us on Tuesday, January 20th, 2026 at 12pm-1pm PST/3pm-4pm EST for the discussion forum: “Trust Starts Upstream: Recommendations for creating ethically sourced health data repositories for AI/ML,” by Dr. Camille Nebeker.

Creating ethically sourced and trustworthy health data repositories is critical for building trustworthy biomedical and behavioral research infrastructure, especially when such repositories underpin machine learning and AI systems. In Ethical sourcing in the context of health data supply chain management: a value sensitive design approach (Nebeker et al., 2025), we integrate value sensitive design (VSD) with concepts from supply chain management to operationalize ethical values across the stages of developing health data repositories. The resulting framework identifies key actors, values (e.g., traceability, security, equity), and tensions that arise during repository creation and highlights practices such as documenting data provenance, articulating expectations for data stewards, and implementing comprehensive privacy and bias mitigation strategies.
During this session, Dr. Nebeker will provide an overview of this framework with a focus on developing a companion checklist that is grounded in VSD and supply chain scaffolding and can be used as actionable guidance in dataset creation.

Bio:
Dr. Camille Nebeker is a professor of public health with appointments in the UC San Diego Design Lab and the Herbert Wertheim School of Public Health and Human Longevity Science. In 2018, Dr. Nebeker co-founded the ReCODE Health center, which is dedicated to conducting cutting edge research to inform ethical practices in digital/AI health research – including machine learning and the use of large language models. The ReCODE Health center supports education and consultation services to guide ethical practices in technology-supported health research across diverse research sectors including traditional academic research and, increasingly, the health technology sector. She’s a principal investigator with the Bridge2AI Bridge Center’s Ethical, Legal and Social Implications Core.

2026 Voice AI Symposium & Hackathon
2026-05-04 - 2026-05-06 | The Vinoy Resort & Golf Club, 501 5th Ave NE, St. Petersburg, FL, 33701, United States
Registration and Call for Science are now open for the 2026 Bridge2AI Voice Symposium + Hackathon! Join us May 4–6, 2026, in beautiful St. Petersburg, Florida, for one of the only global events dedicated entirely to voice biomarkers.
The Voice AI Symposium has become an internationally-recognized event and one of the only conferences focused solely on the research, development, and implementation of voice biomarkers to improve healthcare. Unlike some large-scale tech conferences, the Voice AI Symposium is intentionally intimate and designed to give every attendee meaningful opportunities to meet pioneers, tastemakers, and thought leaders shaping the future of healthcare through voice AI technologies. Building on the success of past years, the 2026 Voice AI Symposium will bring together the forefront innovators, researchers, clinicians, entrepreneurs, and industry leaders of the voice biomarker space for a two-day immersive experience at the forefront of voice and AI in healthcare.

Highlights will include:

Hands-on Training & Workshops using the Bridge2AI-Voice dataset and tools.
Podium presentations selected through an open Call for Science
Interactive Panels & Keynotes featuring international experts and visionaries in healthcare and AI.
Tech Fair & Demos where attendees can experience cutting-edge applications of voice AI.
Pitch Competition spotlighting groundbreaking startups.
Networking Events designed to foster real connections across disciplines.
and more!

Submit your most innovative research and technology under this year’s theme:
“Translating AI Research into Reality: Implementing Voice Biomarkers for Transformative Healthcare.”
Key Dates:
Abstract Submission Deadline: December 5, 2025
Notification of Selection: January 15, 2026
Symposium + Hackathon: May 4–6, 2026
Don’t miss your chance to be part of this global movement at the intersection of voice, data, and health innovation — and to connect with the community shaping the next era of healthcare AI.
Register early to secure your spot — there is limited capacity!
Voice AI Symposium: https://www.eventsquid.com/event.cfm?id=29517
Call for Science: https://www.eventsquid.com/event.cfm?id=30025
