Responsible Artificial Intelligence for Veterans Act of 2026

the adequacy of technological infrastructure supporting deployment and scalability;

the interoperability of the systems with electronic health records, medical devices, pharmacy systems, and other relevant platforms; and

the effect of the system on clinical workflows, staffing models, and operational processes.

the clarity of responsibility for system outputs, errors, and adverse outcomes;

policies governing consent, data use, secondary use, and veteran data protections;

lifecycle management processes, including model updates, retraining, and version control;

oversight mechanisms, internal controls, and audit structures; and

compliance with applicable Federal privacy, cybersecurity, and health information laws.

quantitative performance metrics, including accuracy, sensitivity, and specificity, as applicable;

the presence of disparate performance across demographic groups and the mitigation of bias;

reliability, repeatability, and reproducibility across facilities and veteran populations;

robustness to data variability and generalizability beyond training environments; and

transparency, explainability, and interpretability of model outputs sufficient to permit clinical oversight.

the risk of patient harm, including failure modes and escalation pathways;

safeguards ensuring that the artificial intelligence system functions as a clinical decision-support tool and does not supplant clinical judgment; and

whether clinicians retain the authority and technical ability to override or contest system outputs.

alignment of the system with the needs, accessibility requirements, and preferences of veterans;

usability within clinical workflows;

the availability of training resources and implementation support for clinical staff; and

the degree of clinician and veteran trust in system outputs.

acquisition, deployment, sustainment, and cloud or compute costs;

evidence of cost-effectiveness or cost-utility relative to clinical outcomes; and

resource implications for staffing, administrative burden, and system maintenance.

whether system logic, data sources, training inputs, and decision pathways are sufficiently documented and auditable; and

whether such documentation is available for oversight review.

the clinical effectiveness of each system in real-world practice, including its performance across diverse veteran populations and care settings;

whether each system improves clinical efficiency, including reductions in administrative burden, wait times, or duplicative services;

the clinical utility of each system in supporting medical decision making and improving veteran care outcomes; and

where applicable, the environmental effect of deployment at scale, including energy consumption and sustainability implications.

In general

An agreement entered into under subsection (a) shall specify that, not later than the date that is one year after the date of the agreement, a federally funded research and development center agrees to submit to the Secretary and to the Committees on Veterans’ Affairs of the House of Representatives and the Senate a report that includes each of the following:

Form

The report required by subparagraph (A) shall be submitted in unclassified and unredacted form, but may include a classified annex.

a response of the Secretary addressing each finding and recommendation contained the report; and

a corrective action plan that includes—

conduct a review of—

provide to the Committees on Veterans’ Affairs of the House of Representatives and the Senate a briefing on the review that includes any recommendations of the Comptroller General for improvement.

the full independent evaluation produced pursuant to subsection (a), including all supporting analyses and technical appendices; and

any subsequent implementation updates to the corrective action plan submitted under paragraph (2), without redaction, except for information that is classified or otherwise protected by law.