Chatting new territory: large language models for infection surveillance from pilot to deployment

Rodriguez-Nava et al. present a proof-of-concept study evaluating the use of a secure large language model (LLM) approved for healthcare data for retrospective identification of a specific healthcare-associated infection (HAI)-central line-associated bloodstream infections-from real patient data for the purposes of surveillance.¹ This study illustrates a promising direction for how LLMs can, at a minimum, semi-automate or streamline HAI surveillance activities.