AI in Healthcare: US Policy, Regulation, and What Ophthalmology Practices Must Do In 2026

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U.S. healthcare AI regulation has moved from guidance to governance. FDA, HHS, CMS, and NIST now collectively expect AI in clinical settings to meet medical-device–level standards for validation, monitoring, transparency, and accountability. For ophthalmology, where AI intersects with imaging, diagnostics, billing, and EHR workflows, this means AI must be implemented with lifecycle controls, interoperability, human oversight, and compliance-by-design. The EHR is now the regulatory surface area for AI. Practices that treat AI as governed clinical infrastructure will gain stability and scale; those that treat it as software risk regulatory, financial, and clinical exposure.

5 mins read

The regulation of artificial intelligence in U.S. health care has shifted from nascent guidance to active governance. Federal agencies—HHS, FDA, CMS, and NIST—are issuing strategy documents, risk frameworks, device guidance, and operational rules that collectively define how AI can be used safely, fairly, and transparently in clinical settings.

For ophthalmology practices, where imaging, diagnostics, and procedural care generate high-volume, high-value data, these policies are not abstract: they determine what tools can be deployed, how they must be validated, and how workflows and revenue streams will be audited.

This article summarizes the current U.S. policy landscape, highlights the most consequential regulatory developments, and translates them into granular implications for ophthalmology leaders.

HHS and a federal push toward coordinated AI governance

HHS has articulated an enterprise-level AI strategy that places governance, reuse, and operational adoption at the center of federal AI activity. The department’s approach emphasizes building inventories of validated use cases, establishing governance controls, and aligning investments to accelerate trustworthy AI across public health and clinical care. That strategic posture signals two things for providers:

1. the federal apparatus will favor reproducible AI implementations with traceable governance records, and
2. public-sector demand for validated AI will increasingly set market expectations.

FDA: lifecycle regulation and medical-device rigor for AI/ML tools

The FDA has advanced its regulatory posture for AI embedded in medical products, moving beyond one-off clearances to lifecycle-based expectations. Recent regulatory work has focused on Software as a Medical Device (SaMD) that uses AI/ML, insisting that manufacturers provide a clear Total Product Life Cycle (TPLC) plan, pre-specified algorithm-change protocols, and post-market performance monitoring.

For ophthalmology—where AI may be used to triage retinal images, quantify OCT features, or flag post-op complications—this means vendors and practices must expect device-style evidentiary standards: clinical validation, documented change control, and ongoing real-world performance data.

NIST: operationalizing trustworthy AI through the AI RMF

NIST’s AI Risk Management Framework (AI RMF) reframes regulatory compliance as continuous risk governance. The AI RMF favors four operational functions—govern, map, measure, manage—and supports domain-specific “profiles” that translate abstract trustworthiness principles into actionable controls.

Ophthalmology practices and their vendor partners should adopt RMF-aligned practices: model cards for transparency, explainability artifacts where clinical decisions are downstream, bias and fairness audits across demographic and device subgroups, and routine performance monitoring. Implementing RMF profiles converts compliance talk into operational checklists that auditors or partners can inspect.

CMS: guidance on responsible use and payer-side scrutiny

CMS has published guidance emphasizing the responsible use of AI, especially generative AI tools and algorithmic decisions that affect payment or coverage. CMS has already clarified how Medicare Advantage organizations may use algorithms in coverage determinations and has produced guidance on safeguarding PHI when using AI tools. The practical implication is clear: any AI that touches claims, prior authorization, or clinical documentation will attract both technical scrutiny and payer scrutiny. For ophthalmology ASCs and clinics that rely on accurate coding and timely claims, integrating AI into documentation or billing routines must be paired with audit trails, human oversight, and clear policies that map AI outputs to clinician decisions.

Device adoption and market reality: ML-enabled products are proliferating

Regulatory clearance data and device approval histories indicate that ML-enabled medical products are proliferating rapidly. Recent analyses show high rates of ML-enabled device clearances, largely through 510(k) pathways for lower-risk classifications, but with a growing corpus of devices that require more stringent evidence.

Ophthalmic imaging applications, automated retinal lesion detectors, and procedural decision aids are part of this surge. Practices must therefore treat off-the-shelf AI features as regulated clinical tools: they require vendor documentation, performance evidence on relevant populations, and operational plans for post-deployment monitoring.

Concrete implications for ophthalmology: integration, validation, and workflow control

1. Integration that preserves provenance. Data provenance and traceability are central to both FDA and HHS expectations. When an AI model consumes OCT volumes, fundus photos, and EHR metadata, the chain of custody for each input must be auditable. This requires integration architecture that timestamps, hashes, and logs data sources; labels device models and acquisition parameters; and preserves the raw image as supporting evidence for any AI conclusion.
2. Pre-deployment validation on relevant cohorts. Performance claims must be validated against local populations or similar clinical settings. Vendors’ validation sets rarely mirror every practice’s demographics or device mix. Practices must insist on pre-installation testing: run the model in silent mode against a labeled local sample, measure sensitivity/specificity, and quantify failure modes tied to device model, patient age, race, or refractive status.
3. Human-in-the-loop controls and override logging. Regulatory guidance emphasizes human oversight for AI that affects care. Implement workflows that present AI outputs as clinician decision support, not automated orders. Log every clinician override with the rationale, and use that log as the basis for post-market performance refinement.
4. Billing and documentation alignment. AI-driven suggestions to codes or problem lists must be mapped explicitly to documentation artifacts that justify billing positions. Implement automated “documentation adequacy” checks that flag missing clinical evidence before claims generation. Retain human sign-off on coding recommendations, and track acceptance rates and denial outcomes for iterative improvement.
5. Continuous monitoring and drift detection. Post-deployment surveillance must be proactive. Deploy statistical monitoring that detects distribution shift in inputs (different OCT device firmware, DICOM variations) and outcome metrics (false positives rising). Couple automated alerts with rapid rollback or quarantine procedures to prevent cascade errors.
6. Equity, bias audits, and demographic safeguards. Agencies and civil-society groups are pushing “equity-first” standards. Run regular subgroup analyses, and document corrective actions. Where disparities are detected, restrict model use until mitigation (retraining, recalibration, or threshold adjustments) is implemented.

Regulatory and policy trends to watch in 2026

Policy calendars indicate several converging directions. HHS is moving toward enterprise AI inventories and governance; FDA is enforcing TPLC and algorithm-change clarity; NIST is promoting operational risk frameworks that are expected to inform enforcement or accreditation; CMS is intensifying expectations for transparency when AI influences payment-related processes; and civil-society advocacy is likely to accelerate equity and explainability requirements. Those confluence points mean practices must adopt governance stacks now—policy-ready documentation, technical controls, and legal-ready contracts with vendors that assign responsibilities for validation, reporting, and patient notifications.

Practical checklist for immediate action

• Inventory any AI/algorithmic tools in use, including embedded imaging features and third-party APIs; record intended use, vendor claims, and validation artifacts.
• Run a local silent-validation campaign on representative datasets before clinical activation; capture performance stratified by device, age, and race.
• Implement logging at every decision point: input provenance, model version, score, clinician action, and override rationale.
• Create a documented escalation path: who quarantines, who notifies vendors, who notifies compliance, and how patient safety incidents are recorded.
• Map AI outputs to billable documentation and build pre-claim adequacy checks to reduce denials.
• Schedule periodic bias audits and publish remediation summaries internally for governance review.

Conclusion

AI regulation in U.S. healthcare has crossed a threshold. What was once guidance is now enforceable expectation. Federal agencies are converging on a shared premise: AI in clinical care must be governed like clinical care itself—validated, monitored, auditable, and accountable. For ophthalmology, a specialty built on imaging, diagnostics, and procedural precision, this convergence carries particular weight. AI tools that touch retinal images, OCT data, clinical documentation, billing workflows, or patient communication are no longer peripheral technologies; they are regulated clinical instruments operating within a tightly defined policy perimeter.

The implication is not restraint, but discipline. Practices that succeed in the AI-powered era will not be those that adopt the most tools, but those that implement governance-first architectures: traceable data pipelines, lifecycle monitoring, human-in-the-loop controls, and compliance-aligned workflows. FDA lifecycle regulation, NIST risk frameworks, CMS payment oversight, and HHS enterprise governance together form a clear signal. AI must be operationally trustworthy, not just technically impressive.

For ophthalmology leaders, this moment demands a reframing of EHR and AI strategy. Implementation is no longer an IT project or a vendor decision. It is a clinical governance decision, a revenue-protection decision, and a long-horizon risk decision. The practices that internalize this shift—treating AI as regulated clinical infrastructure rather than optional enhancement—will define the next decade of specialty care. Those that do not will face increasing regulatory friction, financial exposure, and operational drag as policy enforcement tightens.

FAQs

1. How is AI in healthcare regulated in the United States today?

AI in healthcare is regulated through a combination of FDA medical device oversight (for AI/ML used in diagnosis or treatment), CMS guidance (for payment, coverage, and documentation), HHS enterprise governance policies, and NIST’s AI Risk Management Framework. Together, these bodies establish expectations for safety, transparency, validation, and accountability.

2. Does FDA regulate all AI used in ophthalmology?

FDA regulates AI that functions as Software as a Medical Device or influences clinical decision-making. AI used purely for administrative tasks may fall outside FDA oversight but can still be scrutinized under CMS, HIPAA, and consumer protection standards if it affects billing, access, or patient outcomes.

3. What does “lifecycle regulation” mean for AI tools?

Lifecycle regulation means AI systems must be monitored beyond initial approval. Vendors and users are expected to track real-world performance, manage algorithm updates, detect drift, and document corrective actions over time rather than relying on one-time validation.

4. How do AI regulations affect EHR and billing workflows

If AI influences documentation, coding, or claims generation, practices must maintain audit trails, human sign-off, and documentation adequacy. CMS guidance emphasizes that AI-assisted decisions cannot replace clinician judgment or justification in reimbursement workflows.

5. What should ophthalmology practices do now to prepare for 2026?

Practices should inventory AI tools, validate performance on local data, implement decision logging, establish governance policies, align AI outputs with billing documentation, and adopt continuous monitoring frameworks aligned with FDA and NIST expectations.