Transforming Healthcare AI

AI Governance in Healthcare

Founded by Dr. Harvey Castro, MD, MBA. We bridge the gap between medical expertise and artificial intelligence innovation, ensuring responsible, ethical, and effective deployment of LLMs in clinical settings.

"The revolution will not be televised, it will be digitized. We prepare healthcare leaders for the AI-driven future."
Clinical Precision
Neural Networks
Network connection icon representing AI healthcare infrastructure
Real-Time Data
Pulse heartbeat icon representing healthcare monitoring
Human-in-the-Loop

Governance & Accountability

Governance is not a bottleneck; it is the foundation of safety. We implement rigorous frameworks where human oversight is total, ensuring AI assists rather than decides.

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.

Clinical AI Literacy

Understanding the tool, not just the hype. We fill the educational void for clinicians, leaders, and policymakers on the practical realities of Large Language Models.

56
%
Adoption Growth
1.47
%
Hallucination Rate
15
+
Error in niche models
For Clinicians
Capability vs. reliability, prompt engineering safety, and privacy protocols.
For Health System Leaders
Organizational readiness, cost/benefit analysis, risk management.
For Policymakers
Regulatory frameworks, accountability standards, and ethical guidelines.
Dr. Harvey Castro, MD, MBA
Strategic Advisor & AI Healthcare Expert

A Human Accountability Layer

Healthcare does not need more algorithms; it needs guardrails. As a Physician Executive, Consultant, and Speaker, Dr. Castro advocates for the responsible adoption of GPT-based technologies. This platform serves as a central hub: human judgment remains sacred, and patient care remains paramount.

5
+
Best Sellers
100
+
Keynote Talks
20
+
Years Experience
Regulatory Landscape

Frameworks

Evidence-based governance frameworks and research-backed approaches to responsible AI deployment.

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.
Ethical directives for U.S. health departments positioning AI as core to healthcare transformation.
Leading AI Governance Frameworks
Framework
Core Focus
Key Recommendations
Hallucination & Inaccuracy
AI generates plausible but false or unsubstantiated information. Studies show hallucination rates of 1.47% in clinical note generation, with some medical models exceeding 15% on analytical tasks.
Human-in-the-Loop (HITL) validation, robust testing protocols, and use of chain-of-thought reasoning to enable self-verification.
Automation Bias
Over-reliance on AI outputs, leading to errors in clinical judgment. Clinicians may accept flawed AI recommendations and cease searching for confirmatory evidence.
Clinician training on AI limitations, accountability frameworks, and system designs that encourage critical evaluation of AI suggestions.
Data Bias & Health Equity
AI models perpetuate or amplify existing health disparities due to biased training data that underrepresents certain demographic groups.
Diverse and representative data sourcing, fairness audits, external validation across different populations, and continuous monitoring.
Liability & Accountability
Lack of clarity regarding who is responsible for AI-related errors - developers, institutions, or clinicians.
Clear governance policies defining liability for developers, institutions, and clinicians, as advocated by organizations like the AMA.
AI Risks & Mitigation Strategies
AI Governance

Comprehensive presentation on AI risks in healthcare, including statistics on adoption growth and hallucination rates.

Risk Mitigation

Framework for implementing responsible AI governance in healthcare organizations, including practical guidelines.

Resources & Books

Early Thought Leadership in Responsible AI.

ChatGPT and Healthcare: The Key to New Future of Medicine
Books
January 2023

ChatGPT and Healthcare: The Key to New Future of Medicine

ChatGPT and Healthcare: Unlocking The Potential Of Patient Empowerment
Books
February 2023

ChatGPT and Healthcare: Unlocking The Potential Of Patient Empowerment

ChatGPT AI and Healthcare: The Key to the New Future of Medicine
Books
Coming Soon

ChatGPT AI and Healthcare: The Key to the New Future of Medicine