Tejani, Ali S.

Agentic artificial intelligence marks a shift from large language models to autonomous, goal-driven clinical assistants. The framework presented in this report outlines implementation in radiology workflows and addresses cybersecurity, bias, and accountability challenges.

Large language models pose unique cybersecurity risks in health care, including vulnerability to malicious attacks and data breaches. This article equips professionals with strategies to mitigate these threats for safe use.

Although generative artificial intelligence can create podcast summaries of radiology articles, summaries are limited by hallucinations and lack of audience appropriateness, requiring further investigation to justify use as an educational tool.