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About the course
Artificial Intelligence in healthcare does not merely optimise processes — it influences diagnoses, treatment pathways, and patient safety.
This capstone is the culminating stage of the C-Lab Institute AI Responsibility pathway. Participants are placed in a healthcare institution that has deployed a clinical AI system to assist with diagnostic decision-making.
Initial results showed efficiency gains.
However, new evidence suggests possible bias across demographic groups, incomplete validation studies, and regulatory review concerns.
- Media scrutiny is increasing.
- Regulators are requesting documentation.
- Clinicians are divided in opinion.
The hospital board must determine whether the system should continue, be restricted, redesigned, or suspended.
This is not a technical audit.
It is a governance decision involving patient safety and public trust.
In healthcare, AI responsibility is not optional.
It is a clinical obligation.
Technology may assist decision-making —
but governance protects patients.Without governance, innovation can harm.
With governance, innovation heals.
What you'll learn
- Risk Analysis: Identify clinical safety threats, demographic biases, and gaps in AI validation studies.
- Regulatory Navigation: Evaluate institutional exposure to regulatory requirements and medical liability.
- Governance Design: Construct oversight committees with clear accountability between clinical and technical staff.
- Operational Safeguards: Implement "human-in-the-loop" protocols and adverse event reporting systems.
- Strategic Decision-Making: Formulate defensible recommendations to continue, restrict, or pause AI deployment.
- Compliance Roadmapping: Develop 12-month monitoring plans to ensure long-term AI transparency and safety.
Requirements
- Programme Alignment: C-Lab AI Leadership Pathway – Responsibility Stage
- Coin Allocation: Responsibility Coins
- Prerequisite: Completion of AI Readiness for Leaders
Course content
Sections: 2
•Activities: 2
•Resources: 0
Capstone Objective
Participants will act as Chair of the Clinical AI Oversight Committee and prepare a formal Regulatory & Board Advisory Memorandum recommending a responsible course of action.
The capstone evaluates the leader’s ability to:
• Identify patient safety and clinical risk
• Assess validation, bias, and model performance concerns
• Evaluate regulatory compliance exposure
• Design oversight, audit, and accountability structures
• Propose monitoring and escalation mechanisms
• Make a defensible deployment / pause recommendation
Award
🏅 300 Responsibility Coins
📜 C-Lab Institute AI Responsibility Capstone Certificate
Primary 3R Dimension: Responsibility (Do)
Progression Path: Leader → Fellow
Enrolment options
AI Responsibility Capstone: Clinical AI Under Regulatory Scrutiny - Healthcare Case Study
Course modified date: 13 February 2026
Artificial Intelligence in healthcare does not merely optimise processes — it influences diagnoses, treatment pathways, and patient safety.
This capstone is the culminating stage of the C-Lab Institute AI Responsibility pathway. Participants are placed in a healthcare institution that has deployed a clinical AI system to assist with diagnostic decision-making.
Initial results showed efficiency gains.
However, new evidence suggests possible bias across demographic groups, incomplete validation studies, and regulatory review concerns.
- Media scrutiny is increasing.
- Regulators are requesting documentation.
- Clinicians are divided in opinion.
The hospital board must determine whether the system should continue, be restricted, redesigned, or suspended.
This is not a technical audit.
It is a governance decision involving patient safety and public trust.
In healthcare, AI responsibility is not optional.
It is a clinical obligation.
Technology may assist decision-making —
but governance protects patients.Without governance, innovation can harm.
With governance, innovation heals.
- Enrolled students: There are no students enrolled in this course.
Guests cannot access this course. Please log in.
- Course Duration: 2 Hours
- Skill Level: Beginner
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