Medical Training AIAI Ethics and Governance in Care
You'll be able to evaluate a clinical AI tool for bias, transparency, and consent implications and help shape governance policy for its use.
What this track covers
This track covers the ethical principles, bias sources, and regulatory landscape relevant to AI tools used in clinical decision-making. It's built for clinicians who need to evaluate AI systems, communicate their limitations to patients, and participate in governance or oversight decisions at their organization.
What you will practice
- Apply beneficence, non-maleficence, autonomy, and justice to an AI-assisted decision
- Spot likely sources of bias in an AI tool's training or validation data
- Explain a black-box AI recommendation to a patient in plain terms
- Identify what disclosure or consent an AI-assisted diagnosis may require
- Map an AI tool's claims against FDA SaMD and HIPAA considerations
Simulator scenario
A virtual patient scenario where an AI decision-support tool flags a high-risk finding that conflicts with the clinician's own assessment, requiring the clinician to reconcile the discrepancy and discuss it with the patient.
Board question topics
- Core ethical principles applied to AI-assisted care
- Recognizing and mitigating algorithmic bias
- Informed consent for AI-assisted diagnosis or treatment
- Regulatory frameworks for software as a medical device
Ask the tutor
The tutor only answers from licensed sources (FDA prescribing information, public guidelines and our own material) and says when it has none. Educational use only, never patient-specific advice.
Free during the beta. Sign in with your email, no password.
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