Protect what matters
Learn to identify unsafe assumptions, weak validation, bias, drift, cyber exposure and workflow failure before technical performance turns into patient, legal or organizational harm.
AICDRM™ prepares senior professionals to evaluate, authorize, govern and defend high-stakes healthcare AI decisions across patient safety, validation, malpractice, regulation, cybersecurity, vendor risk, governance and insurance.
For Physicians, Healthcare Executives, Legal Counsel, Risk Leaders, Insurers & Clinical AI Governance Professionals
Healthcare AI is no longer only a technology question. It is a patient-safety question, a liability question, an enterprise-governance question and a financial-risk question. AICDRM is designed for the people who may ultimately have to approve an AI system, challenge it, insure it, defend it—or stop it.
Learn to identify unsafe assumptions, weak validation, bias, drift, cyber exposure and workflow failure before technical performance turns into patient, legal or organizational harm.
Develop a shared decision language across medicine, law, operations, risk, insurance, vendors and boards—so AI governance is not delegated blindly to one department.
Build evidence-linked artifacts, governance logic and a final Clinical AI Safety Case™ that force the question senior leaders eventually face: approve, condition, pilot, pause, reject, rollback or retire?
Move beyond “the model is accurate.” Learn calibration, subgroup performance, automation bias, human oversight, decision authority and the documentation needed when AI influences care.
Understand what boards and executive teams must demand before deployment: governance, validation, vendor diligence, accountability, monitoring, incident command and risk-adjusted value.
Connect AI behavior to standard of care, causation, malpractice exposure, informed consent, documentation, contracting, regulatory change and defensible institutional process.
Evaluate residual AI risk, control quality, third-party exposure, claims implications, risk transfer, financial impact and the evidence needed to distinguish governable risk from unpriced uncertainty.
Build practical systems for approval authority, lifecycle monitoring, drift, vendor risk, change control, incident response and executive oversight across an AI portfolio.
Gain a cross-disciplinary framework for teaching and evaluating clinical AI beyond a single specialty—connecting technical performance to clinical consequences, law, governance and enterprise risk.

Official AICDRM™ course textbook
Integrated into weekly study and the evidence-grounded learning environment.
It is for professionals whose judgment carries consequences. The objective is not to turn a physician into a programmer, an attorney into a data scientist or a CEO into an engineer. The objective is to give each of them enough cross-disciplinary command to recognize weak evidence, ask better questions, challenge unsafe assumptions and participate credibly in decisions that may affect patients, institutions and financial risk.
One-time single-seat enrollment provides access to the protected 20-week professional course and its applied learning system.