Enhancing Guardrails for Safe and Secure Healthcare AI

TL;DR

This paper discusses the importance of specialized safety mechanisms for healthcare AI, proposing enhancements to existing guardrails to address hallucinations, misinformation, and factual accuracy, thereby improving patient safety.

Contribution

It introduces tailored improvements to guardrails frameworks like Nvidia NeMo Guardrails for healthcare-specific safety and security challenges.

Findings

Enhanced guardrails reduce misinformation spread in healthcare AI.

Strengthened safety measures improve AI reliability in clinical settings.

Proposed framework modifications better address healthcare-specific safety needs.

Abstract

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.