Before AI adds to the chart,
your rules should apply.
An AI tool can write a note in seconds. Your practice should decide whether it can save that note, needs your review, or must stop. C_Verified is designed to put that checkpoint before the medical record.
What gets checked?
Picture an AI scribe asking to save a visit note. Before that request can continue, the checkpoint is designed to answer five questions using the rules your organization has set.
- Who sent it?
- Identify the tool and the action it is asking to take.
- Is it allowed?
- Check whether that tool has permission to make this change.
- What supports it?
- Check for the source information and review evidence your rules require.
- Has it changed?
- Check that the submitted note matches the content being evaluated.
- Does it meet your rules?
- Apply your practice's requirements, including when a clinician must review the request.
Three possible results
- VerifiedThe request meets the configured requirements and may continue through the approved workflow.
- HeldThe request pauses. A person needs to review it or provide missing information.
- DeniedThe request fails a required check and stops at the checkpoint.
Verified means the request passed those checks. It does not mean the note is medically correct or replace your clinical judgment.
Follow a note from draft to decision.
Suppose your practice requires a clinician to review every AI-written visit note before it is saved. Here is how that rule fits into the proposed workflow.
- The scribe asks to save a draft. The request includes the note, the tool's identity, and the information needed to check it.
- The checkpoint checks your rule. Permission to draft a note does not automatically give the tool permission to save it.
- Missing review puts the request on hold. A clinician reviews the draft. The request must still pass the required checks before it can continue. A failed requirement can also result in denial.
- The decision gets a record. The design calls for a receipt showing what was requested, which rules were used, what evidence was checked, the result, and the time.
The receipt is designed to make later changes detectable, so a team can investigate what happened. It cannot guarantee that a system will never be compromised.
This example describes the product design. Available connections and controls depend on implementation and testing.
Make the rules clear before something goes wrong.
AI governance is how a practice decides which tools it uses, what those tools may do, and who steps in when something goes wrong. It should answer questions your team encounters every day.
- Who owns the problem? Name the person responsible for each tool, its approvals, and reported issues.
- Can we see what happened? Keep a record of where AI was used, what it was asked to do, and who reviewed the work.
- Does it work for our patients? Test performance across relevant patient groups and investigate differences that could cause harm.
- What happens when it fails? Test failure cases, protect access, and give the team a way to report problems and stop unsafe use.
- Where does patient information go? Decide which data a tool may use, who can access it, and how long it may be kept.
- When must a person step in? Set clear review requirements and give reviewers the information and authority to pause or reject an action.
- Is it still working as expected? Recheck tools after updates and as your patients, data, and workflows change.
A checkpoint addresses one part of that work. Your organization still needs training, tool evaluation, privacy practices, and a process for handling incidents.
A note can affect more than documentation.
Once text enters the medical record, another clinician may rely on it. It may also feed a referral, a claim, or later decisions. That is why the point before an AI tool changes the record deserves a clear check.
Healthcare guidance addresses the wider responsibilities around that moment: patient safety, privacy, testing, and continued oversight.
WHO: keep people and patients at the center
WHO's 2021 guidance sets out six ethical principles for health AI. Its 2024 guidance addresses generative models that can work with text, images, and other data. These are recommendations for responsible use, not approvals of individual tools.[1][2]
FDA: a tool's intended use matters
FDA's January 2026 final guidance explains which clinical decision-support functions fall outside the medical-device definition and which remain subject to device oversight.[3] Its separate January 2025 guidance on managing AI-enabled device software throughout its life cycle is still a draft, marked not for implementation.[4]
CHAI and Joint Commission: put oversight into practice
CHAI provides a Responsible AI Guide and checklists for evaluating and using health AI.[5] Its September 2025 guidance with Joint Commission emphasizes organizational policies, local validation, and monitoring.[6]
2026 research: make governance usable
A review in npj Digital Medicine examined 35 healthcare AI frameworks and proposed five stages of governance maturity. It highlights a practical problem: smaller organizations need approaches they can use with the staff and resources they have.[7]
What the standards and laws mean in 2026.
A framework offers guidance. A management standard sets requirements an organization can be assessed against. A law creates obligations for the organizations and activities it covers.
NIST: a voluntary way to manage AI risk
The AI Risk Management Framework helps organizations organize responsibility, understand risks, evaluate them, and decide what to do. NIST also published a generative AI profile in 2024. In 2026, NIST says AI RMF 1.0 is being revised and has released a concept note for a critical-infrastructure profile; that note is not a finished replacement framework.[8]
ISO/IEC 42001: requirements for managing AI
ISO/IEC 42001:2023 covers how an organization establishes, maintains, and improves an AI management system. It addresses responsibilities, risk processes, and ongoing improvement. Certification concerns that management system; it does not prove that every AI output is correct.[9]
EU AI Act: legal duties with different start dates
The Act's requirements depend on the system's use and the organization's role. Transparency rules took effect on August 2, 2026. The European Commission's current timeline places specified high-risk uses under the rules from December 2, 2027, and high-risk systems embedded in regulated products from August 2, 2028, following the AI Omnibus changes that entered into force on July 27, 2026.[10]
These dates do not mean every healthcare AI tool has the same classification or deadline.
Sources and further reading
Reviewed September 7, 2026. Source publication dates are listed below. External links open in a new tab.
- World Health Organization. Ethics and governance of artificial intelligence for health. 2021.
- World Health Organization. Guidance on large multi-modal models. 2024.
- U.S. Food and Drug Administration. Clinical Decision Support Software. Final guidance, January 2026; reissued January 29, 2026.
- U.S. Food and Drug Administration. AI-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations. Draft guidance, January 2025.
- Coalition for Health AI. Responsible AI Guidance, Guide, and Checklists.
- Joint Commission and Coalition for Health AI. Initial guidance to support responsible AI adoption. September 17, 2025.
- npj Digital Medicine. Advancing healthcare AI governance through a comprehensive maturity model based on systematic review. 2026.
- National Institute of Standards and Technology. AI Risk Management Framework and current development updates. AI RMF 1.0: 2023; generative AI profile: 2024; revision and critical-infrastructure work: 2026.
- International Organization for Standardization. ISO/IEC 42001:2023, AI management systems. December 2023.
- European Commission. AI Act and implementation timeline. Updated August 3, 2026.