Abstract
Fever in the returning traveller is a challenging presentation that requires a wide array of clinical expertise and draws on complex medical decision-making. Recent advancements in artificial intelligence have shown it can be a helpful clinical support tool in this context. This scoping review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines and registered on the Open Science Framework (
https://doi.org/10.17605/OSF.IO/BSPKX
), aimed to identify the current state of artificial intelligence use in assessing fever in the returning traveller. Here we describe the three eligible studies evaluating three distinct AI approaches identified in the literature. These heterogenous studies reported promising performance on study-specific endpoints in controlled settings. Given the lack of external validation, narrow populations studied, and absence of real-world implementation or user acceptance testing, further research is needed to explore this technology in a real-world clinical setting. The limited available evidence suggests that AI may have future potential to support assessment of fever in the returning traveller; however, clinical utility, safety, and real-world effectiveness remain unestablished. Large language models represent an emerging area of interest but were not evaluated in the studies included in this review.