为医疗AI设计更可靠的防护机制，防止幻觉与错误信息。
Enhancing Guardrails for Safe and Secure Healthcare AI
- 改进现有防护框架，增强医疗场景下的事实准确性
- 针对医疗问答中的幻觉问题提出针对性加固方案
- 适合关注医疗AI安全性的开发者与临床应用者
生成式AI在解决全球医疗可及性挑战方面潜力巨大，已在多个医疗领域实现创新应用。然而，这些领域专用AI解决方案的大规模采用面临重大障碍：缺乏强有力的安全部署机制，难以有效应对幻觉、误导性信息和真实性保障等问题。若不加管控，这些风险可能危及患者安全并削弱对医疗AI系统的信任。尽管通用框架如Llama Guard可用于过滤有害内容，但无法充分满足医疗场景中对真实性和安全性的严苛要求。本文分析了医疗AI特有的安全与隐私挑战，尤其聚焦于幻觉风险、信息误传及临床环境中的事实准确性需求。提出对现有防护框架（如Nvidia NeMo Guardrails）进行优化，以更好适配医疗专用需求。通过强化这些防护机制，旨在确保AI在医疗领域的安全、可靠与准确应用，降低信息误导风险，提升患者安全。
原文摘要 · Abstract (English)
Generative AI holds immense promise in addressing global healthcare access challenges, with numerous innovative applications now ready for use across various healthcare domains. However, a significant barrier to the widespread adoption of these domain-specific AI solutions is the lack of robust safety mechanisms to effectively manage issues such as hallucination, misinformation, and ensuring truthfulness. Left unchecked, these risks can compromise patient safety and erode trust in healthcare AI systems. While general-purpose frameworks like Llama Guard are useful for filtering toxicity and harmful content, they do not fully address the stringent requirements for truthfulness and safety in healthcare contexts. This paper examines the unique safety and security challenges inherent to healthcare AI, particularly the risk of hallucinations, the spread of misinformation, and the need for factual accuracy in clinical settings. I propose enhancements to existing guardrails frameworks, such as Nvidia NeMo Guardrails, to better suit healthcare-specific needs. By strengthening these safeguards, I aim to ensure the secure, reliable, and accurate use of AI in healthcare, mitigating misinformation risks and improving patient safety.
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