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Medical Devices

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Photo of Jessica Falcone
Program Director
Division of Discovery Science and Technology (Bioengineering) Program Area: Medical Devices
This program supports the development and demonstration of broadly applicable biomedical devices to enable new paradigms of human health.

Emphasis:

The emphasis is on the development of medical device hardware, software, and models to improve patient health. 

NIBIB interests include but are not limited to: 

  • implantable bioelectronic stimulators and sensors for monitoring and modulating human physiology
  • wearable sensors for monitoring health vitals
  • micro devices and injection systems for therapeutic delivery 
  • anti-bacterial and anti-coagulating coatings for implantable devices
  • biohybrid devices for replacing organ function

Related News

NIBIB in the News
November 26, 2024

A team of researchers at the University of California San Diego has developed a new and improved wearable ultrasound patch for continuous and noninvasive blood pressure monitoring. Their work marks a major milestone, as the device is the first wearable ultrasound blood pressure sensor to undergo rigorous and comprehensive clinical validation on over 100 patients. Source: UC San Diego. 

Science Highlights
November 4, 2024

NIBIB has designed an initiative called Enhancing Biomedical Engineering, Imaging, and Technology Acceleration (eBEITA) at HBCUs. Recently, NIBIB made its first round of eBEITA grants to two HBCUs.

Press Releases
October 31, 2024

NIH has announced winners of the RADx® Tech Fetal Monitoring Challenge, a $2 million prize competition to speed development of innovative medical technologies for fetal health diagnosis, detection and monitoring.

Science Highlights
October 21, 2024

NIBIB bioengineer Kaitlyn Sadtler has flourished as a leader of many impactful, interdisciplinary studies. For her role in shaping the future of medical research, TIME magazine has named Kaitlyn Sadtler to the TIME100 Next 2024 List.

Science Highlights
October 3, 2024

As AI is deployed in clinical centers across the U.S., one important consideration is to assure that models are fair and perform equally across patient groups and populations. To better understand the fairness of medical imaging AI, researchers trained over 3,000 models spanning multiple model configurations, algorithms, and clinical tasks. Their analysis of these models reinforced some previous findings about bias in AI algorithms and uncovered new insights about deployment of models in diverse settings.