Large language models (LLMs) hold immense promise for advancing clinical workflows, yet their deployment in healthcare raises critical safety, ethical, and bias-related concerns that exceed the scope of standard red‑teaming practices. In this talk, we first review the fundamentals of general‑purpose LLM red teaming—targeting misinformation, offensive speech, security exploits, private‑data leakage, discrimination, prompt injection, and jailbreaking vulnerabilities. Building on these foundations, we then describe two healthcare‑specific extensions developed by Pacific AI:
- Medical Ethics Red Teaming
We introduce novel test cases derived from core AMA medical‑ethics principles to probe LLM behaviors around physician misconduct, patient autonomy and consent, conflicts of interest, and stigmatizing language. Examples include attempts to coerce consent for unnecessary procedures, fabricate arguments for upcoding, and manipulate clinical documentation for financial gain. - Cognitive‑Bias Red Teaming
We demonstrate targeted benchmarks designed to elicit and measure clinically dangerous biases such as anchoring, confirmation, framing, primacy/recency effects, and ideological alignment, that can distort diagnostic reasoning and treatment recommendations. Through scenario‑based assessments (e.g., risk ‑communication framing, order‑set anchoring), we quantify model susceptibility to contextual and statistical framing errors in healthcare contexts.
This webinar is designed for healthcare technology leaders, clinical AI researchers, and compliance officers seeking practical guidance on evaluating and governing AI tools; attendees will learn actionable red‑teaming strategies and receive ready‑to‑implement test cases to bolster model safety, ethics compliance, and bias mitigation in clinical settings.
FAQ
What makes red teaming in healthcare AI different from other sectors?
Healthcare AI demands protection against risks like data privacy breaches, harmful clinical advice, mis-interpretation of medical content, and hallucinations. Unlike generic AI systems, testing must account for high stakes, patient safety, and domain-specific failures.
Who should be involved in healthcare AI red teaming?
Effective red teams combine clinicians and AI engineers. Clinician expertise is crucial to spot unsafe or misleading outputs in clinical contexts, which may be missed by purely technical review.
What vulnerabilities are commonly uncovered in healthcare LLMs during red teaming?
Dynamic healthcare red-teaming has exposed high failure rates: despite models achieving over 80% MedQA accuracy, up to 94% fail robustness tests, 86% leak private information, 81% display bias, and 66% hallucinate in adversarial scenarios.
What frameworks support structured red teaming for clinical AI?
The proposed PIEE framework offers a structured, multi-phase process for clinical AI red teaming—designed to be accessible to both clinicians and informaticians, enabling collaboration without requiring deep AI expertise.
Why is dynamic, automated red teaming critical for healthcare AI?
Static benchmarks quickly become outdated and may miss real-world vulnerabilities. Dynamic, automated red-teaming—using evolving adversarial agents—continuously stress-tests systems for risks including privacy leaks, unfair bias, and hallucinations—capturing emergent threats in real time.
