safety and red teaming

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Safety & Red-Teaming

The Safety & Red-Teaming track covers how healthcare professionals help identify and mitigate risks in AI systems — both in the content AI produces and in how AI systems respond to adversarial inputs.

Content Safety vs. Response Safety

AI safety in healthcare spans two related dimensions:

Content Safety

Content safety focuses on ensuring that AI outputs do not contain harmful, misleading, or inappropriate information. In a healthcare context, this includes:

• Medically inaccurate claims that could mislead patients or clinicians

• Inappropriate clinical recommendations (e.g., suggesting contraindicated treatments)

• Failure to include necessary safety disclaimers

• Outputs that could cause psychological harm (e.g., detailed self-harm content)

Response Safety

Response safety focuses on how an AI system behaves when prompted in unusual or adversarial ways. A safe AI system should:

• Decline to produce genuinely dangerous content

• Maintain consistent behavior regardless of how a prompt is phrased

• Not be manipulated into bypassing its own safety guidelines

• Handle sensitive healthcare topics (e.g., medications, procedures) responsibly

What is Red-Teaming?

Red-teaming is a structured adversarial testing process where trainers deliberately attempt to elicit unsafe, harmful, or policy-violating outputs from an AI model. The goal is to find failure modes before deployment.

Red-teaming tasks may include:

• Crafting prompts designed to bypass safety guidelines

• Testing model behavior with edge-case clinical scenarios

• Identifying inconsistencies in how a model handles similar sensitive topics

• Documenting failure modes with supporting rationale

Why Healthcare Professionals Are Essential for AI Safety Testing

General red-teamers can probe for generic harms. Healthcare professionals can probe for the specific, high-stakes failure modes that matter in clinical settings:

• Clinical plausibility — A response may pass a general safety check but contain a dosing error only a pharmacist would catch

• Specialty-specific risks — Radiology AI, psychiatric AI, and pediatric AI each have distinct failure modes requiring specialty expertise

• Patient harm scenarios — Anticipating how a patient might act on incorrect AI-generated advice requires clinical experience

• Regulatory awareness — Understanding what AI claims are and are not appropriate under medical and regulatory standards

Courses in This Track

Course	Topics	Duration
Safety 1	Content safety principles, identifying unsafe outputs	TBD
Safety 2	Red-teaming methodology, adversarial prompting	TBD

To access Safety & Red-Teaming courses:

1. Go to Learn in the left sidebar.

2. Scroll to the Safety & Red-Teaming section.

3. Click Start Lesson on any course card.