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Division of Health Informatics Technologies (Informatics)

The focus of the division is to support development of science and technology for processing and evaluating complex biomedical information in order to develop solutions to real-world healthcare problems. This research builds toward practical, patient-centered applications such as:

  • clinical decision-making support systems 
  • in-home treatment monitoring
  • medical image improvement through advanced methodologies
  • next-generation intelligent image and data analysis tools
  • mobile health.
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Behrouz Shabestari ,
Ph.D.
Behrouz Shabestari
Director - National Technology Centers Program
 Acting Director Division of Health Informatics Technologies (Informatics) Program Area: Artificial Intelligence, Machine Learning, and Deep Learning, Image Processing, Visual Perception and Display, Optical Imaging and Spectroscopy

Program Areas

Artificial Intelligence, Machine Learning, and Deep Learning

Artificial intelligance

Digital Health - Mobile Health and Telehealth

Connected Health

Biomedical Informatics

Surgeon looking at a set of data

Point of Care Technologies - Diagnostics

Point of Care

Image Processing, Visual Perception and Display

Image Processing

Bioanalytical Sensors

Sensors

Collaborations

  • Collaborative Research in Computational Neuroscience (CRCNS) – This program supports collaborative research projects and the sharing of data and other resources for the study of computational neuroscience. Activities span a broad spectrum of computational neuroscience research based on the missions and strategic objectives of participating agencies including NIH, National Science Foundation, and the Department of Energy, along with international collaborators. Info and how to apply. Contact: Qi Duan.  
  • Neuroimaging Informatics Tools and Resource Clearinghouse (NITRC) – NIBIB staff lead a trans-NIH team that manages a clearinghouse for tools and resources used by neuroimaging informatics researchers and tool developers. In addition, NITRC helps create and support a community of neuroimaging informatics researchers. Contact: Rui Pereira de Sa.
  • NIBIB Point-of-Care Technologies Research Network (POCTRN) – This network of centers was created to drive the development of appropriate point-of-care diagnostic technologies through collaborative efforts that simultaneously merge scientific and technological capabilities with clinical need. Info at POCTRN. Contact: Tiffani Lash.
  • Smart and Connected Health Program – This is an interagency collaboration which supports the development and use of innovative approaches for transforming healthcare from reactive and hospital-centric to preventive, proactive and person-centered and focused on well-being rather than disease. Contact: Qi Duan.
  • RadLex Ontology – This is a project developed by the Radiological Society of North America (RSNA) through NIBIB funding for establishing a controlled terminology for radiology and serves as a single unified source of radiology terms for radiology practice, education, and research. When complete, the RadLex Ontology will be capable of describing all the salient aspects of an imaging examination, including modality, technique, visual features, anatomy, findings, and pathology.
  • National Centers for Biomedical Imaging and Bioengineering (NCBIB) – Through the P41 grant mechanism, these Centers create critical and unique technology and methods at the forefront of their respective fields and apply them to a broad range of basic, translational and clinical research. Info at NCBIB. Contact: Behrouz Shabestari

Division Staff

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Behrouz Shabestari ,
Ph.D.
Behrouz Shabestari
Director - National Technology Centers Program
 Acting Director Division of Health Informatics Technologies (Informatics) Program Area: Artificial Intelligence, Machine Learning, and Deep Learning, Image Processing, Visual Perception and Display, Optical Imaging and Spectroscopy
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Tiffani Bailey Lash ,
Ph.D.
Dr. Tiffani Bailey Lash
Program Director
 Division of Health Informatics Technologies (Informatics) Program Area: Digital Health - Mobile Health and Telehealth, Point of Care Technologies - Diagnostics
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Qi Duan ,
Ph.D.
Qi Duan
Program Director
 Division of Health Informatics Technologies (Informatics) Program Area: Biomedical Informatics, Image Processing, Visual Perception and Display
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Shawn Mulvaney ,
Ph.D.
man smiling
Health Science Administrator
 Division of Health Informatics Technologies (Informatics) Program Area: Bioanalytical Sensors, Point of Care Technologies - Diagnostics
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Rui Carlos Pereira de Sá ,
Ph.D.
Rui Sa
Program Director
 Division of Health Informatics Technologies (Informatics) Program Area: Artificial Intelligence, Machine Learning, and Deep Learning
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Asha Storm ,
Ph.D.
smiling person with U.S. flag in background
Scientific Program Analyst
 Division of Health Informatics Technologies (Informatics)
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Feben Zenebe ,
Feben Zenebe
Program Analyst
 Division of Health Informatics Technologies (Informatics)

Related News

  • NIH announces prize winners in year-long challenge to develop fetal diagnostic and monitoring technologies

    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.

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  • Researchers develop 3D atlas of the developing mammalian brain

    A team of researchers at Penn State College of Medicine and collaborators from five institutes have developed a new 3D atlas of developing mice brains using advanced imaging and microscopy techniques. The new high-resolution maps of the mouse brain will help advance the understanding of brain development and the study of neurodevelopment disorders. 

    Source: Penn State Research News

     

    NIBIB in the News

  • Implementing medical imaging AI: issues to consider

    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.

    Science Highlights

  • New AI model efficiently reaches clinical-expert-level accuracy in complex medical scans

    UCLA researchers have developed a deep-learning framework that teaches itself quickly to automatically analyze and diagnose MRIs and other 3D medical images – with accuracy matching that of medical specialists in a fraction of the time.  Source: UCLA Computational Medicine News

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