2025 News
Bridge2AI Raw Audio Data Access
News from: Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information v2.0.1.
Sept. 11, 2025
The published Bridge2AI-Voice dataset contains derived features from the audio waveforms. Interested users can request access to the original raw audio data by contacting: DACO@b2ai-voice.org
The raw audio data will be disseminated through controlled access only to protect participant's privacy.
Roger Mark and George Moody Receive the 2026 IEEE Biomedical Engineering Award
Aug. 18, 2025
Each year, the IEEE Awards Board selects a distinguished group of individuals to receive IEEE’s highest honors, recognizing exceptional achievements and significant contributions to technology, society, and the engineering profession.
We are honored to share that Professor Roger G. Mark and the late George B. Moody have been named co-recipients of the 2026 IEEE Biomedical Engineering Award for their leadership in ECG signal processing and the creation and distribution of curated biomedical and clinical data. View announcement on the IEEE website.
This recognition highlights the profound and lasting impact that Roger Mark and George Moody have had on biomedical engineering and the global research community. Their vision and contributions continue to underpin our work on PhysioNet and databases such as MIMIC.
About Roger G. Mark
Roger G. Mark is Distinguished Professor of Health Sciences and Technology Emeritus at the Institute for Medical Engineering & Science at MIT. His work spans physiological signal processing, patient monitoring, and critical care decision support. He is the co-founder of PhysioNet, launched in 1999 to provide open access to physiologic signals, clinical data, and open-source software for the research community.
About George B. Moody
George B. Moody made transformative contributions to biomedical signal processing through his work in electrocardiography. He developed the WFDB libraries and much of the code available on PhysioNet, which remains essential for ECG signal processing worldwide. He also created and led the PhysioNet/Computing in Cardiology Challenges for 15 years, fostering global collaboration and innovation.
Access Restrictions Under DOJ Data Security Program
July 15, 2025
PhysioNet has introduced updated access policies for certain datasets to comply with the U.S. Department of Justice’s Data Security Program (DSP) under Executive Order 14117. The DSP final rule took effect on April 8, 2025 and full enforcement began July 8, 2025: https://www.justice.gov/opa/media/1396351/dl
The DSP imposes export-control–style restrictions on U.S. persons sharing or transferring bulk sensitive personal data (e.g., genomic, biometric, health, financial, geolocation) and U.S. government-related data with specified countries or "covered persons". The rule applies to interactions with countries including: China (including Hong Kong and Macau), Cuba, Iran, North Korea, Russia, and Venezuela, as well as individuals or entities connected to them.
PhysioNet now prevents access to certain controlled-access datasets for users connecting from IP addresses or affiliations in those regions, or for those classified as “covered persons”. These steps are taken to satisfy legal obligations and are not a judgment on your work as researchers.
We understand these changes may affect ongoing research. PhysioNet is committed to supporting your efforts to understand the policy and explore compliant access options.
Further information
- DOJ DSP Compliance Guide and FAQs: https://www.justice.gov/opa/media/1396351/dl
- NIH Notice NOT‑OD‑25‑083, which imposes similar NIH-specific restrictions: https://grants.nih.gov/grants/guide/notice-files/NOT-OD-25-083.html
This repository is under review by NIH for potential modification in compliance with U.S. federal Administration directives.
March 27, 2025
This repository is under review by NIH for potential modification in compliance with U.S. federal Administration directives.
BioNLP @ACL 2025 Shared Task on Grounded Electronic Health Record Question Answering (ArchEHR-QA)
Feb. 26, 2025
The overarching goal of the ArchEHR-QA 2025 (pronounced "Archer") shared task is to develop automated responses to patients' questions by generating answers that are grounded in key clinical evidence from their electronic health records (EHRs). The proposed dataset, ArchEHR-QA, comprises hand-curated, realistic patient questions (reflective of patient portal messages), relevant focus areas identified within these questions (as determined by a clinician), corresponding clinician-rewritten versions (crafted to aid in formulating responses), and note excerpts providing essential clinical context.
Read more: https://doi.org/10.13026/zzax-sy62
New Dataset: Bridge2AI-Voice v1.0 Now Available on PhysioNet
Feb. 4, 2025
We are pleased to announce the release of Bridge2AI-Voice v1.0, a dataset designed to advance research into the use of voice as a biomarker of health. This dataset, developed as part of the NIH Bridge2AI initiative, aims to support artificial intelligence research by providing ethically sourced, high-quality voice-derived data linked to clinical information.
Bridge2AI-Voice v1.0 includes 12,523 voice-derived recordings from 306 participants across five North American sites. Participants were selected based on conditions known to affect vocal characteristics, including:
- Voice disorders (e.g., laryngeal conditions affecting phonation)
- Neurological and neurodegenerative disorders (e.g., Parkinson’s, ALS, stroke)
- Mood and psychiatric disorders (e.g., depression, anxiety)
- Respiratory disorders (e.g., asthma, chronic cough)
- Pediatric voice and speech disorders
The initial release does not include raw voice recordings. Instead, it provides derived acoustic features, such as spectrograms, along with detailed demographic, clinical, and validated questionnaire data.
Read more: https://doi.org/10.13026/37yb-1t42
The George B. Moody PhysioNet Challenge 2025 has begun
Jan. 21, 2025
This year's Challenge focuses on detecting Chagas disease from ECGs. Chagas disease is a parasitic disease in Central and South America that affects an estimated 6.5 million people and causes nearly 10,000 deaths annually. Timely treatment may prevent or slow damage to the cardiovascular system, but serological testing capacity is limited, so detection through ECGs can help to identify potential Chagas patients for testing and treatment.