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Introduction <p>The emergence of large language models (LLMs) provides new avenues for clinical support in healthcare and dentistry. However, these models often exhibit unpredictable behaviours when challenged by adversarial or misleading inputs. Recent data indicate that nearly 20% of LLM outputs contain safety risks or biases, necessitating rigorous evaluation prior to clinical use. This review examines AI red teaming, a systematic approach for identifying system vulnerabilities through simulated attacks. It details methodological approaches and outcome measures while proposing a structured framework to integrate these safety evaluations into the clinical AI lifecycle.</p> Methods <p>This review focuses on prompt-based attacks, such as prompt injection and jailbreaking, which are highly relevant in medical settings. It evaluates various testing strategies, including manual expert reviews, automated “attacker” models, and hybrid human-in-the-loop systems. A lifecycle-based framework is introduced, utilizing the collaborative “red-blue-purple” teaming model. This approach spans pre-deployment testing, live deployment monitoring, and iterative review audits to ensure that clinical guardrails remain robust against evolving adversarial tactics.</p> Conclusion <p>Safe implementation of LLMs in dentistry and healthcare requires continuous, iterative adversarial testing rather than static assessments. Success depends on standardized protocols, multidisciplinary collaboration between clinicians and AI researchers, and the development of domain-specific benchmarks. Bridging existing regulatory gaps through these structured frameworks is vital for ensuring LLMs are safe, reliable, and clinically fit for patient care.</p>

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Evaluating the safety of large language models in healthcare and dentistry: adversarial testing approaches

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

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  • Fahad Umer,
  • Muhammad Muthar Shaikh,
  • Absar Ur Rahman

摘要

Introduction

The emergence of large language models (LLMs) provides new avenues for clinical support in healthcare and dentistry. However, these models often exhibit unpredictable behaviours when challenged by adversarial or misleading inputs. Recent data indicate that nearly 20% of LLM outputs contain safety risks or biases, necessitating rigorous evaluation prior to clinical use. This review examines AI red teaming, a systematic approach for identifying system vulnerabilities through simulated attacks. It details methodological approaches and outcome measures while proposing a structured framework to integrate these safety evaluations into the clinical AI lifecycle.

Methods

This review focuses on prompt-based attacks, such as prompt injection and jailbreaking, which are highly relevant in medical settings. It evaluates various testing strategies, including manual expert reviews, automated “attacker” models, and hybrid human-in-the-loop systems. A lifecycle-based framework is introduced, utilizing the collaborative “red-blue-purple” teaming model. This approach spans pre-deployment testing, live deployment monitoring, and iterative review audits to ensure that clinical guardrails remain robust against evolving adversarial tactics.

Conclusion

Safe implementation of LLMs in dentistry and healthcare requires continuous, iterative adversarial testing rather than static assessments. Success depends on standardized protocols, multidisciplinary collaboration between clinicians and AI researchers, and the development of domain-specific benchmarks. Bridging existing regulatory gaps through these structured frameworks is vital for ensuring LLMs are safe, reliable, and clinically fit for patient care.