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ARPA-H PRECISE-AI Performance and Reliability Evaluation for Continuous Improvement of AI-Enabled Medical Tools is sponsored by Advanced Research Projects Agency for Health (ARPA-H). This program develops capabilities to detect when AI-enabled tools used in real-world clinical care settings are potentially out of alignment with their underlying training data and auto-correct these tools to maintain peak performance.
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ARPA-H launches program to help AI-enabled medical tools maintain peak performance | ARPA-H ARPA-H launches program to help AI-enabled medical tools maintain peak performance ARPA-H launches program to help AI-enabled medical tools maintain peak performance Program aims to develop techniques to automatically identify and correct performance degradation of AI-enabled clinical decision support tools The Advanced Research Projects Agency for Health ( ARPA-H ), an agency within the U.S. Department of Health and Human Services (HHS), today announced a new funding opportunity through the Performance and Reliability Evaluation for Continuous Modifications and Useability of Artificial Intelligence (PRECISE-AI) program.
PRECISE-AI intends to develop capabilities that can detect when an AI-enabled tool used in real-world clinical care settings is potentially out of alignment with its underlying training data and, importantly, auto-correct these AI-enabled tools to maintain their peak performance, making them more helpful to clinicians and beneficial to patients.
Today, more than 950 medical devices with integrated AI functionalities have been authorized by the U.S. Food and Drug Administration (FDA)—a tenfold increase from 2018 to 2023. These new AI-enabled tools transform doctors’ ability to provide care, from virtual assistants to enhanced medical imaging and diagnostic tools.
However, research suggests that Machine Learning models (ML) used in medical AI-enabled tools may degrade over time due to changes in input data—such as changes in clinical operations, data acquisition, patient population, or even IT infrastructure. Current manual techniques make it challenging to monitor and maintain the performance of these AI-enabled tools in real-world settings.
“The promise of AI-enabled tools for health care is only as strong as the relevant real-world data informing them,” said PRECISE-AI Program Manager Berkman Sahiner . “The inability to automatically monitor and maintain an AI-enabled medical device’s performance based on real-world operations creates risks for clinicians and patients.
PRECISE-AI aims to create a suite of techniques that can maintain peak performance of AI-enabled tools by recommending the optimal approach to detect and mitigate the underlying AI model performance degradation so that clinicians can provide better patient care. ” PRECISE-AI will develop techniques that analyze AI-enabled tools and identify root causes for performance deterioration.
The root cause analysis will inform self-corrective actions to improve the AI-enabled tool’s performance. The program will also create mechanisms for notifying clinicians, AI tool developers, hospital administrators, and regulators when performance degradation occurs.
“PRECISE-AI is addressing a growing gap in ensuring AI tools used in clinical decision-making are accurate, safe, and robust in real-world settings,” said ARPA-H Director Renee Wegrzyn, Ph. D . “In doing so, ARPA-H is creating a foundation of trust between clinicians and these AI tools, which will further expand AI’s potential in improving health outcomes for all Americans.
” Through a forthcoming program solicitation, PRECISE-AI will bring together ML experts, health information specialists, and clinicians to develop novel techniques across five technical areas (TAs). Performer teams will start by developing tools to establish the most accurate estimate of a patient’s diagnosis given the available evidence or the “ground truth.
” Teams will then develop autonomous methods to monitor an AI tool’s performance against these “ground truths,” determine the root causes of any degradation, and make necessary corrections.
Additionally, teams will develop methods for the underlying AI model to communicate uncertainty to clinicians and other users, as well as develop the data infrastructure to allow findings to be shared with other clinicians, developers, and hospital administrators using the AI-enabled tool. The progress of all the performers will be confirmed by an independent verification and validation team.
Multiple awards under this solicitation are anticipated. Awards will depend on the quality of the proposals received and the availability of funds. Learn more about PRECISE-AI on its program page , including information about Proposers’ Day registration and guidance on submitting a teaming profile.
Based on current listing details, eligibility includes: Open to interdisciplinary teams including ML experts, health information specialists, and clinicians from academia, industry, nonprofits, and small businesses. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Not specified Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
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The ARPA-H CIRCLE (Critical Illness Immunological Reprogramming and Control Point Learning Engine) program aims to develop AI-driven digital twin capabilities for treating critical illness in ICU patients. Clinicians currently lack tools to track rapidly changing immune responses where excessive inflammation can cause organ failure and death. CIRCLE seeks to create computational models of individual immune systems that provide actionable insights and enable better deployment of existing and next-generation immunotherapies for critically ill patients. The program addresses three technical areas: creating datasets characterizing critical illness progression in real time; developing patient-specific digital twin computational immune system models using AI/ML; and testing immune system modulation approaches with individualized treatment strategies. Solution summaries are due March 30, 2026, with full proposals due May 28, 2026. Teams may include academic institutions, non-profits, corporate entities, or combinations. This is a currently open solicitation posted on SAM.gov.
ARPA-H's CIRCLE program develops new AI-driven digital twin capabilities to treat critical illness by tracking and modulating the body's immune response at scale. The program integrates three technical areas: creating real-time datasets characterizing critical illness progression and resolution, developing patient-specific computational digital twin models of immune systems using AI, and testing approaches to modulate immune response for individualized treatment. CIRCLE seeks integrated Measure-Model-Modulate systems with a performance target of reducing average ICU stay by 25%. The program aims to give clinicians AI-based tools for better diagnosis and care of critically ill patients, progressing toward FDA-compliant systems and clinical adoption within 3-5 years. Solution summaries were due March 30, 2026, with full proposals due May 28, 2026.
-Purpose. This Funding Opportunity Announcement (FOA) encourages Small Business Innovation Research (SBIR) grant applications from small business concerns (SBCs) that propose to develop, standardize, and validate new and innovative assays, integrated strategies, or batteries of assays that determine or predict specific organ toxicities (e.g., ocular, dermal, hematotoxicity, cardiotoxicity, gastrointestinal toxicity, hepatotoxicity, nephrotoxicity, ototoxicity, olfactory loss, bladder toxicity, neurotoxicity, pulmonary toxicity, endocrine toxicity, and pancreatic beta cell toxicity), resulting from both acute and chronic exposures to various chemicals, environmental pollutants, biologics and therapeutic molecules or drugs. In addition, this FOA encourages the development, standardization, and validation of new models of arthritis, convulsion, infection and shock. New approaches for high throughput toxicity screening that involves the use of molecular endpoints, computer modeling, proteomics, genomics and epigenomics and the development of virtual tissues are also encouraged as are development of 3-dimensional organ models for toxicity evaluation. -Mechanism of Support. This FOA will utilize the SBIR (R43/R44) grant mechanisms for Phase I, Phase II, and Fast-Track applications and runs in parallel with a FOA of identical scientific scope, PA-09-007, which encourages applications under the Small Business Technology Transfer (STTR) (R41/R42) grant mechanisms. Funding Opportunity Number: PA-09-006. Assistance Listing: 93.113,93.173,93.361,93.389,93.837,93.846,93.847,93.848,93.849,93.859,93.867. Funding Instrument: G. Category: ED,ENV,FN,HL.
Purpose. This Funding Opportunity Announcement (FOA), issued by the National Cancer Institute (NCI), National Institutes of Health (NIH), invites Small Business Innovation Research (SBIR) cooperative agreement applications from small business concerns (SBCs) that propose to develop new, or to improve existing application(s) of nanotechnology-based therapeutics or/and in vivo diagnostics. This FOA will specifically support pre-clinical optimization and testing of these cancer-relevant nanotechnology applications against the intended cancer type. The proposed projects must be milestone-driven and must be clearly directed toward development of an ultimate commercial product. The outcomes are expected to advance the discovery and pre-clinical optimization phase so that an Investigational New Drug (IND) or Investigational Device Exemptions (IDE) application could be submitted to the Food and Drug Administration (FDA) by the end or shortly after completion of the Phase II project period. To facilitate these steps, the NCI will assist the awardees in various ways, including the support through the NCI-sponsored Nanotechnology Characterization Laboratory. This FOA will NOT support basic research projects, studies on disease mechanisms, and clinical trials. Mechanism of Support. This FOA will utilize the SBIR (U43/U44) cooperative agreement mechanisms for Phase I and Phase II applications. Funds Available and Anticipated Number of Awards. Awards issued under this FOA are contingent upon the availability of funds and the submission of a sufficient number of meritorious applications. The total amount awarded and the number of awards will depend upon the quality, duration, and costs of the applications received. Funding Opportunity Number: PAR-10-286. Assistance Listing: 93.393,93.394,93.395,93.396. Funding Instrument: CA. Category: ED,HL. Award Amount: Up to $150K per award.
This Funding Opportunity Announcement (FOA) invites Small Business Innovation Research (SBIR) grant applications from small business concerns (SBCs) for funding to perform research leading to the development of innovative technologies that may advance progress for early detection and assessment of individuals at risk and for early diagnosis, prognosis and follow-up of type 1 diabetes (T1D). Funding Opportunity Number: RFA-DK-15-024. Assistance Listing: 93.847. Funding Instrument: G. Category: FN,HL. Award Amount: $2M total program funding.