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 $1,000,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports the development of generative imaging models to verify and explain machine learning systems for healthcare applications. The key goals include: Developing robust "robustness audits" using synthetic data to assess how well a healthcare deep learning system will operate at different clinical sites, given variability in...
This National Science Foundation (NSF) Project Grant award, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $855,000 in funding to the Texas A&M Engineering Experiment Station (Tees) to develop a bimodal interpretable multi-instance medical image classification framework. The research aims to create a more scalable, interpretable, and robust artificial intelligence (AI) system to better analyze complex medical images, such as from multiple patient...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, with a funding amount of $200,000, will support the development of concept-based reasoning approaches to improve interpretability and accountability of deep neural network (DNN) models for healthcare applications. The key research tasks under this 2-year award (10/1/2025 - 9/30/2027) are: (1) building inherently explainable concept-based DNN models for medical diagnosis, and (2)...
This $175,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports research to improve the efficiency and reliability of explainable AI (XAI) systems. The project aims to accelerate computationally intensive XAI algorithms, develop unified explainer models, and validate the methods in medical applications like histopathology imaging and cancer prognosis. This work will establish a...
This Project Grant from the National Science Foundation Division of Information and Intelligent Systems provides $625,000 to Duke University to develop an interpretable artificial intelligence framework for improving care of critically ill patients. The framework incorporates novel matching techniques known as Almost-Matching-Exactly to analyze observational data from patient treatment and emulate a randomized controlled trial. By matching each treated patient to similar untreated patients,...
This $255,807 National Science Foundation project grant will fund the development of an AI-assisted software system to accelerate the labeling of medical tomographic images. Administered through the NSF Directorate for Engineering's Engineering program (CFDA 47.041), the grant aims to extract new information from medical images and improve patient outcomes. Alienbyte Scientific Software Inc. will apply machine learning algorithms to create an adaptive system that evolves to increase the speed,...
This Project Grant award of $304,929.00 from the National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) program supports the development of new artificial intelligence (AI) models that utilize causal understanding and reasoning. The project aims to create a methodology for developing AI models that can provide reliable, traceable, and human-comprehensible analytics and decision-making for healthcare applications. The goal is to overcome the limitations of...
The National Science Foundation awarded a $598,676 project grant to Beth Israel Deaconess Medical Center, Inc. to develop machine learning-driven user interfaces for medical record information gathering and synthesis under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The award period is from September 1, 2022 to August 31, 2026. The project will advance the foundations of human-AI interaction and artificial intelligence for healthcare by developing...
This Project Grant from the National Science Foundation Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), provides $570,102 to the University of Utah from September 1, 2022 to August 31, 2026. The award will support the development of methods to improve the interpretability and reliability of deep learning models for medical imaging applications. Specifically, the University of Utah researchers will develop a...
This Project Grant award of $499,997.00 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports the development of a new framework to improve the interpretability and steerability of domain-specific AI models in medical imaging.
The key products and services to be delivered include:
Constructing an anatomically aware vision-language model capable of encoding and generating 3D medical images and radiology reports, to reduce the risk of AI "hallucination".
Developing methods to break down existing medical AI systems into understandable symbolic rules and programs that reflect how the models make decisions, enabling clinicians and researchers to identify errors and guide the system's behavior.
Evaluating the approach using large-scale data on breast cancer and chronic lung disease, with the goal of improving fairness and reliability in medical AI across diverse patient populations.
This award aims to transform clinical AI practices by embedding clinicians more directly in the model development and deployment cycle. The project will be conducted by the Trustees of Boston University over a 5-year period from September 1, 2025 to August 31, 2030.