The Research Resource for Complex Physiologic Signals

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.

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.

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.

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.

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.