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Transparent Foundations: Post-Editing Foundation Models for Explainable and Deployable Radiology Imaging AI

Award Number: R21EB038753
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/18/2026
PERIOD OF PERFORMANCE END DATE: 07/31/2028

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Transparent Foundations: Post-Editing Foundation Models for Explainable and Deployable Radiology Imaging AI - Project summary/abstract Radiology imaging is a cornerstone of modern healthcare, providing accessible and cost-effective diagnostics. The recent integration of deep learning, particularly through foundation models, has increasingly enhanced diagnostic precision, and as of December 2024, over 1,000 AI-enabled medical devices were FDA-approved, with more than 75% are used in radiology. Despite their promise, foundation models face concerns regarding: (1) the lack of transparency in their decision making, which makes it difficult for clinicians to understand or verify, and (2) limitations in pretraining data with long-tailed pathologies that can affect downstream reliability and generalizability on rare or differential cases. In response to PA-25-304 (Exploratory/Developmental Research), this project will develop and validate new AI approaches to enhance the transparency and clinical generalizability of foundation models in imaging diagnostics without requiring full model retraining. While the focus of this application has been on lung disease detection on chest X-rays, the methodologies can be disease and image agnostic. Based on our preliminary data and our experience with an interdisciplinary team of data scientists and radiologists, we plan to execute specific aims: 1) Integrating clinically interpretable concepts to enhance model transparency, and 2) studying model-editing methods to tackle “long-tailed pathologies”. The studies proposed in this project are novel and innovative because they address critical gaps in understanding and enhancing the transparency and generalizability of medical foundation models. Aim 1 will be the first to audit and curate expert- driven concept sets for radiological findings and improve model transparency through concept embeddings. Aim 2 will be the first study to examine and enhance performance on rare pathologies, as well as differential diagnoses, through surgical adjustments and few-shot model editing. Our framework offers scalable, actionable, and easily checkable solutions that align foundation models with clinical decision-making processes, ensuring outputs are both transparent and clinically meaningful while maintaining diagnostic utility. The expected outcome of this project is a holistic framework to ensure transparency, explainability, and deployability in foundation models for medical imaging. The success of this project will provide critical insights into radiological risk estimation by (1) enhancing the transparency and explainability of model decision-making, and (2) developing model-editing techniques that maintain diagnostic accuracy across a wide range of pathologies. This project is highly feasible and has the potential to be transformative for both AI-driven data science and clinical medicine.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $244,897 )
2026	2026	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	8/18/2026	NEW	$244,897
														Subtotal = $244,897

Grand Total All Awards = $244,897

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Transparent Foundations: Post-Editing Foundation Models for Explainable and Deployable Radiology Imaging AI

Award Number: R21EB038753

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/18/2026

PERIOD OF PERFORMANCE END DATE: 07/31/2028

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