Healthcare AI Regulation 2025: New Compliance Requirements Every Provider Must Know

Understanding AI in Healthcare Regulation and the Compliance Crisis Facing Medical Providers

Artificial intelligence in healthcare is no longer a futuristic concept; it’s reshaping the medical practice today. With 46% of U.S. healthcare organizations currently implementing generative AI technologies, the question is no longer whether to adopt AI, but how to do so legally and safely. For hospitals, health systems, and medical practices navigating this transformation, understanding the evolving regulatory landscape for healthcare AI has become mission-critical for both patient safety and legal protection.

The regulatory challenge is unprecedented. The vast majority of medical AI is never reviewed by a federal regulator, and probably no state regulator either. This regulatory gap creates significant liability exposure for healthcare organizations deploying AI systems without proper oversight frameworks.

Joint Commission and CHAI Release Groundbreaking AI Governance Framework

In September 2025, the Joint Commission partnered with the Coalition for Health AI (CHAI) to release the first comprehensive guidance for responsible AI adoption across U.S. health systems. This landmark collaboration between the accrediting body for over 23,000 healthcare organizations and a coalition representing nearly 3,000 member organizations signals a fundamental shift in how healthcare AI compliance will be evaluated.

The guidance addresses a critical market need: practical, actionable direction for organizations at any stage of their AI journey. As Dr. Jonathan Perlin, president and CEO of the Joint Commission, noted, AI is changing healthcare at an unprecedented scale, and organizations urgently need structured frameworks to navigate this transformation responsibly.

The Regulatory Dilemma: Who Should Oversee Healthcare AI?

The question of healthcare AI regulation authority remains contentious and unresolved. Federal agencies possess limited statutory authority to regulate AI tools comprehensively. The FDA has issued non-binding guidance documents, but putting every AI system through the rigorous FDA approval process for drugs or medical devices would be prohibitively expensive and slow for many innovations.

The Biden administration proposed “assurance labs”—private-sector organizations partnering with government to vet algorithms under agreed-upon standards. However, political transitions have left the future of centralized AI oversight uncertain. What remains clear is that individual healthcare organizations bear immediate responsibility for AI governance, regardless of how federal policy evolves.

Seven Critical Pillars of Healthcare AI Compliance

The Joint Commission-CHAI guidance establishes seven fundamental areas that healthcare organizations must address when adopting AI-driven tools. These represent both compliance requirements and potential sources of legal liability:

1. AI Policy and Governance Structures for Healthcare Organizations

Healthcare providers must establish clear AI governance policies with oversight mechanisms involving executive leadership, regulatory and ethical compliance teams, IT departments, cybersecurity experts, safety personnel, and relevant clinical departments. Effective AI governance in hospitals requires documented decision-making processes that include input from all stakeholders, particularly patients and communities affected by AI deployment. Organizations lacking formal governance structures face heightened risk if AI systems produce adverse outcomes.

2. Local Validation of AI Systems in Clinical Settings

Generic vendor validation proves insufficient for healthcare AI compliance. Organizations must validate AI tools within their specific deployment context (accounting for unique patient populations, clinical workflows, and operational environments) before clinical implementation. This requirement for local AI validation in healthcare settings is non-negotiable and ongoing, not a one-time checkbox exercise. Failure to conduct proper local validation before deploying AI diagnostic tools or clinical decision support systems creates substantial malpractice exposure.

3. Data Stewardship, Privacy, and HIPAA Compliance for Healthcare AI

Healthcare AI data privacy requirements extend far beyond basic HIPAA compliance. Organizations must ensure responsible management of patient data used in AI systems, with particular attention to data provenance, quality, and security. When protected health information is involved, healthcare entities must establish appropriate business associate agreements with AI vendors and implement robust data protection protocols including encryption, strict access controls, regular security assessments, and incident response plans. HIPAA breach notification requirements apply if AI systems expose patient data, creating potential liability for organizations with inadequate safeguards.

4. Transparency and Informed Consent Requirements for AI in Patient Care

Healthcare AI transparency regulations increasingly require that patients and staff understand when and how AI is being used. Organizations must develop clear communication strategies about AI deployment in clinical settings and ensure accessible information about AI tools and governing policies. Several states, including California, have enacted laws requiring healthcare providers to disclose AI use in patient care and obtain explicit consent before utilizing AI-powered systems. Non-compliance with these AI disclosure requirements in healthcare can result in regulatory penalties and patient lawsuits.

5. Addressing Bias and Health Equity in AI Healthcare Systems

Bias in healthcare AI systems represents perhaps the most legally complex compliance area. Organizations must actively identify, assess, and mitigate biases that could lead to health disparities or discriminatory outcomes. The Office for Civil Rights (OCR) has made clear that uses of AI in healthcare cannot discriminate on the basis of race, age, sex, or other protected characteristics. Risk and bias assessment must occur during local validation and continue through ongoing monitoring. This creates both an ethical imperative and a potential source of legal liability under federal nondiscrimination laws if AI systems produce inequitable outcomes for protected patient populations.

6. Continuous Quality Monitoring and Performance Evaluation of Healthcare AI

Healthcare AI monitoring requirements shift compliance from one-time implementation review to continuous oversight. Organizations must establish risk-based processes to monitor and evaluate AI tool performance on an ongoing basis, scaled to the setting and proximity to patient care decisions. During AI vendor procurement, organizations should require detailed information about how tools were tested and validated, how biases were evaluated and mitigated, and whether vendors will perform validation using samples representative of the deployment context. Post-deployment monitoring, validation, and testing activities must be documented and maintained.

7. Voluntary AI Safety Event Reporting in Healthcare

The guidance encourages knowledge sharing across the healthcare industry by reporting AI-related safety events to independent organizations. Organizations can utilize existing structures such as the Joint Commission’s sentinel event process or confidential reporting to federally listed Patient Safety Organizations, creating collective learning mechanisms while potentially obtaining certain reporting protections. As regulatory scrutiny intensifies, documented participation in voluntary safety reporting may demonstrate good faith compliance efforts.

State-Level Healthcare AI Regulation: A Patchwork of Requirements

While federal healthcare AI policy remains fragmented, states have moved aggressively to fill the regulatory void. More than half of U.S. states have introduced or passed bills specifically addressing healthcare AI regulation, creating a complex patchwork of compliance requirements.

California has emerged as a leader in AI healthcare legislation. Assembly Bill 3030 requires healthcare providers to disclose AI use in patient care and obtain explicit consent before utilizing AI-powered systems. Senate Bill 1120 mandates that qualified human reviewers must oversee utilization review and medical necessity determinations, ensuring that healthcare AI systems cannot make coverage decisions solely through automation.

Illinois amended its Managed Care Reform and Patient Rights Act to address AI in prior authorization processes, though it allows either healthcare professionals or accredited automated processes to certify medical necessity. New York’s pending Assembly Bill A9149 would require health insurers to conduct clinical peer review of AI-based decisions, disclose AI use publicly, and submit algorithms and data sets to state regulators for certification that they won’t result in discrimination.

These state healthcare AI laws create compliance challenges for multi-state health systems and create potential conflicts with federal policy frameworks. Organizations must monitor state-specific requirements and adapt policies accordingly.

The Resource Challenge: Small Hospitals and AI Compliance Costs

A troubling equity issue emerges from the Joint Commission-CHAI guidance: compliance burden falls heavily on individual facilities. As Cohen explained in the Journal of the American Medical Association, the cost of evaluating and monitoring AI systems on a hospital-by-hospital basis can be significant, creating a disparity where some hospitals can afford proper oversight and others cannot.

This raises both practical and ethical concerns. It would be problematic if effective AI systems that could provide the most benefit in lower-resource settings cannot be implemented because those settings cannot meet regulatory requirements. Moreover, if AI models are trained on data from patients across the country, many of those patients may never benefit from the models their data helped create if their local healthcare facilities cannot afford compliance.

Healthcare organizations facing resource constraints should consider collaborative approaches, including participation in shared AI validation efforts, industry consortiums for AI oversight, or engagement with emerging third-party assurance organizations that could provide validation services at scale.

Healthcare AI and the “Race Dynamic”: When Innovation Outpaces Ethics

Cohen identifies what he calls a “race dynamic” in healthcare AI development—whether racing to be first to market, racing against startup funding depletion, or racing between countries for AI supremacy. These time and urgency pressures make it easier to overlook ethical issues and proper validation.

This race dynamic is particularly concerning for healthcare AI systems that interface directly with consumers, such as mental health chatbots, where internal hospital review may not occur at all. These direct-to-consumer AI healthcare tools can scale extremely quickly without substantive internal review, creating significant potential for harm.

Healthcare organizations procuring AI systems should inquire specifically about the development timeline and validation processes vendors employed. Evidence of rushed development or inadequate testing should raise red flags about reliability and safety.

Practical Steps for Healthcare AI Compliance

While the Joint Commission-CHAI guidance is currently non-binding, it clearly signals future accreditation requirements and regulatory expectations. Healthcare organizations that proactively align with these principles position themselves advantageously as oversight bodies move toward formalized AI governance standards.

1. Conduct Comprehensive AI Inventory Assessments: Organizations should identify all AI-driven tools currently deployed or under consideration, including systems embedded in electronic health record platforms, scheduling algorithms, diagnostic support tools, and administrative automation. Many healthcare providers significantly underestimate the extent of AI already operating in their facilities.
2. Strengthen AI Governance Infrastructure: Review and enhance governance structures to ensure appropriate oversight of AI initiatives. Establish clear lines of accountability, define roles and responsibilities for AI oversight, and ensure decision-making bodies include diverse clinical, technical, ethical, and legal expertise.
3. Create Audit-Ready AI Documentation: Develop comprehensive documentation of AI adoption decisions, validation processes, monitoring activities, bias assessment efforts, and adverse event investigations. As regulatory scrutiny increases, the ability to demonstrate systematic, thoughtful approaches to AI governance will prove invaluable in defending against claims of negligence or discrimination.
4. Review and Strengthen AI Vendor Contracts: Healthcare organizations should review existing contracts with AI tool providers and establish procurement standards requiring vendors to provide comprehensive information about tool development, testing, validation, bias mitigation, and ongoing support for local validation efforts. Contracts should clearly allocate responsibilities for monitoring, updating, and addressing performance issues. Indemnification provisions should be carefully negotiated to address potential liability for AI system failures.
5. Implement Staff Education Programs for Healthcare AI: Ensure healthcare workers understand when AI is being used, how it functions within their workflows, the limitations of AI systems, and where to access information about organizational AI policies and procedures. Different tools may require different training levels, and organizations should assess these needs systematically. Inadequate staff training on AI systems creates patient safety risks and potential liability for organizations.
6. Establish Continuous Monitoring Systems: Implement ongoing monitoring mechanisms to track AI system performance, identify potential biases or errors, and detect performance degradation over time. Monitoring should be risk-based, with more intensive oversight for AI systems involved in high-stakes clinical decisions.

The Future of Healthcare AI Regulation: What’s Coming

The Joint Commission and CHAI plan to release additional detailed playbooks soon, followed by a voluntary AI certification program in 2026. These developments signal that AI governance will become an increasingly prominent component of healthcare accreditation and regulatory compliance.

Federal policy remains in flux. The Trump administration has signaled a preference for minimal AI regulation and has issued executive orders aimed at limiting state AI regulatory authority. However, executive orders cannot preempt state laws in this manner, and the tension between federal and state healthcare AI policy will likely persist.

The Centers for Medicare & Medicaid Services has launched pilot programs applying AI in prior authorization decisions for traditional Medicare, and HHS has indicated commitment to being “all in” for AI across federal health programs. Once AI-enabled prior authorization becomes normalized and vendors can point to it as the federally accepted standard, state regulation may become more difficult.

Healthcare organizations must monitor both federal and state regulatory developments and adapt compliance strategies as the landscape evolves.

Why Healthcare Organizations Need Specialized Legal Counsel for AI Compliance

The intersection of AI technology, healthcare delivery, and regulatory compliance creates extraordinarily complex legal challenges requiring specialized expertise. Whether your organization is beginning to explore AI applications or managing multiple AI-driven systems, ensuring compliance with emerging healthcare AI regulations and protecting against potential liability demands careful legal analysis.

Questions healthcare organizations face include: How should vendor contracts allocate liability for AI system failures? What documentation is necessary to demonstrate adequate bias testing? How can organizations comply with varying state disclosure requirements? What governance structures provide adequate oversight while enabling innovation? How should organizations respond to AI-related adverse events? What insurance coverage addresses AI-specific risks?

These questions require attorneys who understand healthcare operations, technology systems, regulatory frameworks, patient safety standards, and evolving legal requirements for AI in medical settings.

Protect Your Organization: Contact Our Healthcare AI Compliance Attorneys

Our healthcare law practice helps hospitals, health systems, and medical practices navigate the legal complexities of AI adoption in clinical and administrative settings. We provide comprehensive guidance on:

• AI vendor contract negotiation and risk allocation
• Governance structure development and policy implementation
• Regulatory compliance with federal and state healthcare AI laws
• HIPAA and data privacy compliance for AI systems
• Bias assessment and health equity requirements
• Risk mitigation strategies and liability protection
• Response to AI-related adverse events and regulatory investigations
• Staff training program development for AI compliance

We understand both the transformative potential of AI in healthcare and the legal frameworks that must guide its responsible implementation. Our attorneys stay current with rapidly evolving regulatory guidance, state legislation, accreditation standards, and enforcement trends.

Don’t wait for regulatory enforcement, patient lawsuits, or adverse events to focus on healthcare AI governance and compliance. The Joint Commission-CHAI guidance, emerging state laws, and federal policy developments make clear that AI oversight expectations are rising rapidly. Organizations that act proactively will be better positioned legally and competitively.

Contact our healthcare AI compliance attorneys today for a confidential consultation. We’ll assess your current AI deployment, identify compliance gaps and legal risks, and develop a practical roadmap for responsible AI adoption that protects patients, supports innovation, and shields your organization from liability. The future of healthcare is being written now—ensure your organization is.