Module 20 Lesson 2: AI security in Healthcare and Medtech

In Healthcare, AI security is not just about data—it's about Patient Safety. A compromised diagnostic model or a leaked patient record can have life-altering consequences.

1. Diagnostic Adversarial Attacks

AI models are increasingly used to analyze X-rays, MRIs, and CT scans to detect diseases like cancer.

• The Attack: Medical Adversarial Examples. An attacker adds "Subtle Noise" to a medical image.
• The Result: The AI misinterprets the image, failing to detect a tumor or incorrectly identifying a healthy patient as sick.
• The Impact: Incorrect treatments, delayed surgeries, or unnecessary psychological distress.

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2. HIPAA and PII in Healthcare LLMs

Healthcare providers are using LLMs to summarize patient notes and assist in clinical documentation.

• The Risk: De-identification Failure. Even if names are removed, an LLM might "Memorize" (Module 12) rare medical conditions or combinations of data that can be used to re-identify a patient.
• The Compliance: Under HIPAA, healthcare AI must use "Business Associate Agreements" (BAAs) with cloud providers and implement strict data isolation.

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3. Securing Medical IoT and AI Agents

AI-powered medical devices (like insulin pumps or cardiac monitors) are becoming "Agentic" (Module 9).

• The Attack: Tool Injection. If an attacker can inject a command into the AI managing a medical device, they could trigger a lethal dosage or disable a life-saving function.
• The Defense: Critical medical functions must have Non-AI Hard-coded Limits. For example, an insulin pump should have a physical mechanical limit that prevents it from delivering a dangerous amount, regardless of what the AI "decides."

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4. Poisoning the Medical Knowledge Base

RAG systems (Module 10) are used by doctors to look up latest medical research.

• The Attack: Scientific Paper Poisoning. Attacker-published "Fake Research" that contains subtle errors in drug dosages or treatment protocols.
• The Result: The AI retrieves these "Facts," and the doctor, trusting the AI, follows a dangerous treatment plan.

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Exercise: The Healthcare Security Lead

1. You are securing an AI that "Summarizes Patient Chats." Why is "Deduplication" especially important for privacy here?
2. Why should medical AI models be "White-box" tested more rigorously than marketing models?
3. How can "Differential Privacy" protect patients in a large-scale medical research dataset?
4. Research: What is "FDA's guidance on ML-enabled medical devices"?

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Summary

Healthcare AI security is a Life-and-Death responsibility. To be successful, you must prioritize Integrity (accurate diagnostics) and Confidentiality (patient privacy) above all else.

Next Lesson: Consumer trust: Securing AI in E-commerce and Retail.