The Compliance Risks of AI Deregulation in Healthcare

Why AI Oversight Matters in Healthcare

Artificial intelligence (AI) is embedded in nearly every aspect of healthcare—from predictive analytics and diagnostic support tools to claims adjudication and patient engagement platforms. Its potential is immense, but so are the risks.

Efforts to deregulate AI or prevent new regulations from being enacted could leave healthcare organizations without clear standards for mitigating these risks. For compliance professionals, the absence of regulatory guardrails would mean greater reliance on internal governance and external frameworks.

The Risks of AI Without Oversight

1. Data Quality & Bias

AI systems depend on large datasets to train and improve performance. If those datasets are incomplete or biased—for example, underrepresenting certain racial or socioeconomic groups—AI outputs can perpetuate or worsen health disparities. Poor data quality can lead to incorrect diagnoses, inappropriate treatments, and liability exposure for the organization.

2. Lack of Transparency (“Black Box”)

Many AI models, particularly deep learning models, are not explainable. Clinicians and compliance officers may have no insight into how an AI-derived recommendation was generated. This lack of explainability undermines trust, makes validation difficult, and can prevent providers from challenging faulty outputs.

3. Regulatory & Legal Compliance Issues

Without oversight, organizations risk inadvertently violating existing standards such as HIPAA, FDA device regulations, or even CMS Conditions of Participation. AI systems could process patient data in non-compliant ways, leading to breaches, fines, and reputational harm. Emerging AI-specific rules could also shift quickly, creating uncertainty and gaps in compliance.

4. Patient Safety

AI errors in diagnosis, treatment planning, or clinical decision support can cause direct harm if not subject to human oversight. In a deregulated environment, patient safety incidents may rise as organizations lack external accountability for the quality of AI recommendations.

5. Cybersecurity Vulnerabilities

AI platforms are often cloud-based and deeply integrated with electronic health records (EHRs) and medical devices. If not properly secured, they are attractive targets for cyberattacks, which could compromise patient data or even alter AI outputs in ways that lead to unsafe care decisions.

6. Over-Reliance on AI

Without explicit standards for clinician accountability, healthcare professionals may defer excessively to AI recommendations—even when those outputs conflict with their clinical judgment. This can result in a loss of critical thinking and increased risk of adverse events.

7. Rapidly Changing Technology

AI models “drift” as data and environments change. Without enforced version controls or validation requirements, organizations could unknowingly use outdated algorithms, resulting in less accurate outputs and increased exposure to errors.

8. Ethical Concerns

Deregulation could exacerbate ethical challenges such as informed consent, use of patient data for AI training, and job displacement among healthcare workers. Ethical missteps erode public trust and can lead to backlash against organizations deploying AI.

9. Liability & Accountability

When AI systems cause harm, who is responsible—the vendor, the clinician, or the healthcare organization? Without clear liability frameworks, deregulation may leave organizations more vulnerable to lawsuits and complicate risk management strategies.

What Should Compliance Professionals Monitor?

1. NIST’s AI Risk Management Framework (RMF) – As formal regulations loosen, NIST’s voluntary framework may serve as the industry standard for evaluating bias, transparency, and security.

2. Updates from CMS, OCR, and OIG – These agencies may embed AI considerations into existing privacy, safety, and fraud oversight structures, even without standalone AI regulations.

3. FDA Updates for AI/ML: Expect continued evolution in adaptive AI oversight, validation expectations, and SaMD clearance processes.

4. Accreditation and Certification Bodies – Expect groups like The Joint Commission and other accrediting entities to fill regulatory gaps with AI governance standards.

5. Litigation and Liability Trends – Watch court cases involving AI errors, as legal precedent will play an outsized role in defining accountability without regulations.

6. Internal Governance Models – Strengthening vendor management, validation, and audit trails will be critical for organizations to self-regulate responsibly.

   
   

Key Takeaways

Deregulating AI would not reduce compliance obligations—it would amplify them. Without clear external standards, healthcare organizations would need to:

• Establish internal AI governance frameworks with robust testing, monitoring, and bias mitigation,

• Conduct ongoing cybersecurity and privacy assessments,

• Maintain audit trails for AI-driven decisions,

• Ensure clinician oversight and accountability, and

• Proactively address ethical, liability, and transparency issues.

  
  

AI is an incredibly powerful tool, but without guardrails, it could increase patient harm, exacerbate inequities, and elevate compliance risks. Compliance professionals must prepare for a future where internal controls, not external regulations, may be the primary line of defense.

Do you have questions about this blog? Please contact jessicazeff@simplycomplianceconsulting.com.