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AI Governance & Ethics in Clinical Settings
Rating: 5.0 out of 5(1 rating)
320 students

AI Governance & Ethics in Clinical Settings

Implement ethical AI frameworks, manage algorithmic bias, and ensure patient safety in clinical environments
Created byLearnsector LLP
Last updated 4/2026
English
English [Auto],

What you'll learn

  • Identify board-level fiduciary responsibilities for clinical AI adoption and institutional risk management
  • Detect and mitigate algorithmic bias within medical datasets to ensure institutional health equity
  • Establish multidisciplinary AI oversight committees to manage the full algorithmic lifecycle
  • Implement Human-in-the-Loop (HITL) standards to maintain clinical safety and physician oversight
  • Analyze medical malpractice liability and legal precedent regarding automated clinical suggestions
  • Develop a Clinical AI Ethics Charter to align technological innovation with the hospital's core mission
  • Monitor for algorithmic performance drift and execute formal decommissioning protocols for unsafe tools
  • Utilize standardized model cards to improve transparency and clinician trust in AI-driven diagnostics
  • Conduct ethical impact assessments to evaluate the long-term societal effects of clinical automation

Course content

4 sections • 8 lectures • 48m total length

  • Fiduciary Duties and AI Risk Management5:34

    Identify the fiduciary duties of governing boards for clinical technology, assess how algorithmic performance drives institutional risk, and establish a multidisciplinary AI oversight committee with governance spanning regulation and monitoring.

  • Regulatory Compliance and Liability Frameworks5:50

    Navigate regulatory compliance and liability frameworks in AI-assisted healthcare, including EU high-risk classifications, FDA software as a medical device, HIPAA, privacy mandates, data governance, and transparency.

  • Knowledge Check

Requirements

  • A foundational understanding of healthcare operations or clinical workflow.
  • Familiarity with basic data privacy concepts (e.g., HIPAA) is beneficial but not required.
  • No prior programming or technical AI development experience is necessary.

Description

“This course contains the use of artificial intelligence.”

As of 2024 and looking toward 2025, the integration of Artificial Intelligence within clinical environments has transitioned from an innovative luxury to an operational necessity. However, the rapid deployment of predictive models and diagnostic software introduces significant institutional risks, ranging from algorithmic bias to complex regulatory compliance challenges. This course provides a comprehensive framework for healthcare executives, clinicians, and administrative leaders to establish robust governance structures that prioritize patient safety and institutional integrity.


The curriculum begins by addressing the governance mandate, detailing the board’s fiduciary responsibilities regarding clinical technology adoption. Participants will analyze the correlation between algorithmic performance and institutional risk, while evaluating domestic and international regulatory frameworks, including the EU AI Act and FDA Software as a Medical Device (SaMD) classifications. This foundation ensures that technology adoption is strategically aligned with the organization's mission and legal obligations.


A significant portion of the course is dedicated to the critical issues of algorithmic bias and health equity. Learners will examine how historical dataset disparities and proxy variables can lead to unintended discrimination. The course provides actionable strategies for implementing equity auditing and continuous monitoring to remediate bias in active clinical tools, ensuring that AI deployments do not widen existing health outcome gaps.


Operational integrity and patient safety are addressed through the lens of Human-in-the-Loop (HITL) standards. The course explores the dangers of automation bias and clinician over-reliance, providing protocols for the decommissioning of underperforming algorithms. Through detailed case studies, including failures in sepsis detection and the impact of alert fatigue, participants will learn to build resilient safety guardrails within digital workflows.


Finally, the course outlines the requirements for institutional transparency and the creation of a Clinical AI Ethics Charter. By standardizing model cards and enhancing explainability, healthcare organizations can foster trust among both clinicians and patients. This program is designed for professional learners seeking a structured, factual approach to the ethical complexities of modern medical technology. The content is updated to reflect the current legislative landscape and the latest standards in clinical informatics and risk management.

Who this course is for:

  • Healthcare Executives and Hospital Board Members seeking to manage institutional risk.
  • Clinical Leads and Department Chairs responsible for technology implementation.
  • Healthcare Compliance, Legal, and Risk Management professionals.
  • Medical Informatics specialists and Digital Health project managers.

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