AI Governance in Healthcare

Establishing the standards, oversight mechanisms, and accountability frameworks that ensure AI serves patients — not replaces clinical judgment.

Human-in-the-Loop

AI should never be the final decision maker. We advocate for "assisted human-centered output" where every suggestion is reviewed by licensed professionals.

Accountability Frameworks

Who is responsible when AI fails? Establishing clear liability protocols for developers, institutions, and clinicians is our priority.

Bias & Hallucination

LLMs can sound confident while being factually wrong. Robust testing identifies "phantom patterns" in clinical data before deployment.

Auditability

Immutability for training and reasoning. All system decisions must track back to source data for transparency.

Framework

Core Focus

Key Recommendations

World Health Organization (WHO)

Ethics, human rights, and LMMs.

Protect autonomy, ensure transparency, promote equity with mandatory audits.

American Medical Association (AMA)

"Augmented Intelligence" to support clinicians.

Implement risk-based oversight, establish clear liability, avoid mandatory AI use without validation.

NIST AI RMF

Voluntary, cross-sector risk management.

Adopt four function core (Govern, Map, Measure, Manage) to create trustworthy systems.

EU AI Act

Risk classification (Low to High).

Prohibits high-risk AI without human oversight in medical contexts; mandates transparency.

HHS AI Strategy

Ethical directives for U.S. health departments positioning AI as core to healthcare transformation.

Leading AI Governance Frameworks

Steps

Our Implementation Approach

Assessment

Evaluate your organization's current AI readiness and risk profile.

Framework

Match the right governance framework to your clinical context.

Implementation

Deploy policies, training, and oversight mechanisms.

Monitoring

Continuous auditing, bias testing, and outcome tracking.

Ready to implement AI governance?

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