Despite several evaluation and oversight frameworks, the U.S. still lacks a dedicated, unified certification mechanism that stamps an AI system as trustworthy across contexts. Authors from an NPJ Digital Medicine study explain this gap further, stating, “While guidelines like SPIRIT-AI, CONSORT-AI, and DECIDE-AI promote algorithmic information reporting in scientific publications for transparency, they lack specific requirements to translate principles into practice.”
U.S. efforts lean toward risk-based governance rather than formal certification. FDA-style approaches in healthcare AI focus on context of use, validation, credibility evidence, and human oversight, especially for models used in drug development and clinical settings. Principles-based frameworks such as FUTURE-AI describe what a trustworthy system should look like (fairness, traceability, robustness).
Ultimately, even with multiple frameworks validating certain AI platforms and potential advancements coming in the near future, AI trustworthiness cannot be fully certified in the United States at present.
AI trustworthiness in the U.S. means a system can be relied on to behave safely and appropriately in a defined context of use. Trustworthiness is framed less as a single label and more as a multi-criteria assurance case built from documentation, testing, governance, and monitoring. For organizations already subject to strict regulatory compliance, like Paubox’s HIPAA compliance status, features like generative AI are more trustworthy.
Trustworthiness is treated as something you earn by showing:
Guidance like FUTURE-AI explicitly pushes a lifecycle view, design, development, validation, regulation, deployment, and monitoring, because trustworthy claims decay when models or environments change. The FUTURE-AI guideline, broken down in a BMJ journal article, makes the supervision point explicit: “Given the high stakes nature of medical AI, human oversight is essential and increasingly required by policy makers and regulators.”
U.S. certification usually means a formal, independent conformity assessment showing a product or process meets defined safety, performance, and regulatory requirements. Healthcare AI rarely gets a standalone “trustworthiness seal” under that model.
Regulatory oversight can still look certification-like in how it controls change over time. FDA’s Predetermined Change Control Plan (PCCP) guidance, for example, is designed to support iterative updates to AI-enabled devices while maintaining reasonable assurance of safety and effectiveness, effectively pre-specifying what can change and how performance will be assessed after updates.
A Frontiers in Digital Health study states, “The addition of generative AI in the healthcare industry is a process that presents many ethical and practical challenges….Examples of these challenges include the lack of transparency, trust, and regulatory laws.” No standardized certification means healthcare teams often have to trust, but verify on their own.
Regulatory uncertainty makes that risk feel even worse because AI updates happen so quickly, rules and guidelines don't always keep up. This makes it hard for organizations to answer the question, What data can we use? Who can we give it to? How do we show that the model still works months later, whether the patient population, workflows, or illness patterns change? Models can drift and get worse in the background when people think they are stable.
Paubox Generative AI shows what trustworthy-by-design looks like. Its HIPAA-aligned approach centers on protecting sensitive data in email security operations through strong controls, clear access boundaries, and audit-ready visibility. People need to trust healthcare AI for more than just smart outputs. Trust depends on whether the system protects privacy, supports accountable decision-making, and stays reliable when conditions change.
In the study Safety and Precision AI for a Modern Digital Health System, authors stressed the need for tools to be engineered safely, “Health focused precision AI will need to be thoroughly designed, tested and managed to prevent inadvertent introduction of safety issues.”
Strong risk management also has to cover the basics, data quality, explainability, where possible, and iterative testing before and after deployment, so performance does not quietly degrade. Hospitals still carry responsibility on the buyer side, too. Vendor maturity, security posture, and ethical alignment should be evaluated upfront, and independent audits plus transparent reporting expectations help close the assurance gap by keeping the focus on patient outcomes rather than unchecked innovation.
See also: HIPAA Compliant Email: The Definitive Guide (2026 Update)
FUTURE-AI is a set of practical guidelines for developing and evaluating trustworthy AI in healthcare. It covers the whole lifecycle.
FUTURE-AI aims to close the trust gap. It takes broad ethics ideas and turns them into concrete checks for safety, fairness, transparency, and real-world reliability in clinical settings.
No. FUTURE-AI is not a certification or an approval label. It’s a framework that helps teams show stronger evidence and better processes, but it does not grant legal or regulatory status.
Because performance and risk depend on where the AI is used. A model might work well in one hospital or patient group and perform poorly in another, so FUTURE-AI treats trustworthiness as tied to the specific setting, workflow, users, and population.