A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models
arXiv:2606.00027v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed across healthcare, yet existing benchmarks fail to capture model behavior under adversarial or ethically complex conditions common in clinical practice. We developed a multi-domain red teaming framework evaluating eleven contemporary LLMs across 690 clinically grounded scenarios spanning nine domains and over 150 subcategories. Scenarios incorporated adversarial transformations, and responses were assessed using a seven-dimension rubric with LLM-assisted scoring and human-in-the-loop validati
The rapid deployment of LLMs in sensitive domains like healthcare necessitates robust safety evaluations, and existing benchmarks are proving insufficient for clinical complexities and adversarial conditions.
This framework addresses critical safety, robustness, and fairness concerns for AI in healthcare, which directly impacts patient outcomes, regulatory acceptance, and the ethical scaling of medical AI applications.
The development of a multi-domain red teaming framework moves beyond theoretical LLM evaluations to address practical, ethically complex, and adversarial scenarios ubiquitous in clinical practice.
- · Healthcare AI developers
- · Patients
- · Regulatory bodies
- · Healthcare providers
- · Under-tested LLM developers
- · Unsafe AI solutions in healthcare
Increased trust and better performance of medical LLMs in real-world clinical settings.
Faster regulatory approval processes for AI solutions that can demonstrate robust safety and ethical compliance.
Shifting the competitive landscape towards AI developers who prioritize and integrate advanced red teaming and safety frameworks into their development cycles.
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Read at arXiv cs.CL