Project Grant R01EB036501
- This $717,712 Project Grant from the National Science Foundation's Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development and evaluation of a knowledge-enhanced and interpretable radiology report generation framework using longitudinal, multimodal electronic health record data and domain knowledge. The awardee, Joan & Sanford I Weill Medical College of Cornell University, will build a...
- This Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) provides $800,000 to develop an intelligent radiology platform through human-machine cooperation. The awardee, Georgia Tech Research Corporation, will create an accurate medical image labeling tool using state-of-the-art artificial intelligence algorithms to maximize accuracy and minimize inter- and intra-reader variability among radiologists. The tool will provide...
- This Project Grant award, funded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), supports the development of a dynamic, customizable, and vendor-neutral patient projection data library and software platform for training and evaluating AI-based algorithms in CT imaging. The $641,063 award will enable the creation of a diverse range of simulated patient...
- This National Science Foundation (NSF) Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $220,000 to Yale University from September 1, 2023 through August 31, 2027. The project aims to develop a smarter artificial intelligence (AI) system to better understand and analyze complex medical images, such as those from multiple scans of a patient. The research team will tackle challenges to make the AI system more scalable, interpretable,...
- This federal Project Grant award of $695,814 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) supports research to develop artificial intelligence (AI) technologies that enable online adaptive radiation therapy for proton therapy. The key products and services to be delivered through this grant include: 1) Developing an asymmetric autoencoder network...
- The federal Project Grant award of $1,148,652 was made on April 1, 2025 by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286). The grant supports research and development of an ultra-high spatial resolution photon-counting computed tomography (CT) system with multiple focal spots. The key products to be delivered include: Developing system models and...
- This $194,022 Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) supports research led by the Massachusetts General Hospital (MGH) to develop advanced deep learning models for predicting therapeutic response in cancer patients with brain metastases. The grant will fund the candidate, an oncologist at MGH, to build upon his expertise in...
- This Project Grant award of $131,800 from the National Institute of General Medical Sciences (CFDA 93.859 - Biomedical Research and Research Training) aims to quantify the effects of precise vs. imprecise text cues and target location cues on radiologist performance and gaze behavior when interpreting chest X-rays and 3D volumetric CT scans. The central hypothesis is that expert radiologists will search more effectively and identify abnormalities more quickly when provided with more precise...
- This federal Project Grant award, titled "Automated Multi-Timepoint CT Analysis for Enhanced Metastatic Disease Evaluation in Colorectal Cancer: An Anatomy-Aware Vision-Language AI Approach," was provided by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286). The $718,456 award, with a performance period from July 1, 2025 to May 31, 2029, supports...
- This federal Project Grant award from the National Science Foundation (CFDA 47.075 - Social, Behavioral, and Economic Sciences) provides $460,999.00 to the Massachusetts Institute of Technology (MIT) to develop a new method for comparing human and artificial intelligence (AI) decision-making. The goal is to generate insights on how to optimally combine human and AI input in high-stakes decisions, such as medical diagnoses. The project will apply this method to compare an AI tool's predictions of...
This Project Grant award of $742,261.00 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), aims to advance head computed tomography (CT) interpretation through three interconnected approaches focused on knowledge representation, image-based report verification, and longitudinal analysis of sequential scans. The project, led by President and Fellows of Harvard College, doing business as Harvard Medical School, seeks to develop a comprehensive knowledge graph and similarity metric for standardizing radiological concepts, leverage large language models to create more granular taxonomies than existing medical ontologies, implement an AI-enabled verification system for real-time assessment of radiology reports, and develop novel methods for automated longitudinal comparison of sequential head CT scans. This work is expected to establish essential foundations for reliable AI-assisted head CT interpretation, enhancing radiologist trust and improving patient care through more accurate and verifiable reporting systems.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $742.3k | 8/5/25 |