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TRUVACE RECORD VERSION
record: TRV-2026-1045
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-10T06:05:26.280224Z
status: published
lens: g_space
sector: health
headline: Artificial Intelligence and Complementary Digital Health Technologies Across the Travel Medicine Continuum: A Narrative Review
dek: Background Artificial intelligence (AI) and complementary digital health technologies are increasingly being applied to improve prevention, diagnosis and surveillance in travel medicine. This narrative review evaluates current applications of these technologies across the pre-travel, peri-travel and post-travel phases of the travel continuum. Methods A narrative literature review was conducted using a clinically oriented three-phase framework encompassing pre-travel preparation, peri-travel monitoring and post-t…
gain_title: AI and complementary digital health technologies improved travel healthcare by enhancing pre-travel risk prediction, enabling earlier outbreak detection during travel, and increasing diagnostic accuracy after travel.
problem_title: (none)
trace_subject: (none)
gain_reading: AI and complementary digital health technologies improved travel healthcare by enhancing pre-travel risk prediction, enabling earlier outbreak detection during travel, and increasing diagnostic accuracy after travel.
gain_evidence: Machine learning improved individualized risk prediction before travel and supported diagnostic decision-making after travel
problem_reading: (none)
problem_evidence: (none)
quick_read: This narrative review examined how AI and complementary digital health tools are being used across pre-travel, peri-travel, and post-travel care, covering machine learning risk models, decision-support systems, large language models, wearables, telemedicine, and outbreak surveillance infrastructure.

By the September 2026 publication date, reported benefits were based on retrospective evaluations showing earlier detection and improved diagnostic metrics, leaving uncertainty about prospective effectiveness, interoperability, and clinical impact when integrated into routine travel medicine practice.
limitation: Evidence base is largely retrospective without prospective multicenter evaluation of clinically meaningful outcomes, limiting conclusions about routine integration.
tag: Evidence-backed gain
key_points: Review used three-phase framework: pre-travel preparation, peri-travel monitoring, and post-travel diagnosis and surveillance. | Technologies reviewed included machine learning, clinical decision-support systems, large language models, wearable IoT, telemedicine, and metagenomic next-generation sequencing. | In hospitalized returned travelers, ChatGPT-4o identified the correct diagnosis in 68% and included it in top three differentials in 78%. | Metagenomic next-generation sequencing increased diagnostic yield by 24.2% beyond routine investigations.
rundown: The narrative review organized evidence by travel continuum, finding clinical decision-support systems enabled more personalized preventive and therapeutic recommendations before travel, while wearable IoT enabled continuous physiological monitoring during travel and mass gatherings.

Post-travel results included the MALrisk model for imported malaria with 0.98 AUC, ChatGPT-4o diagnostic accuracy in returned travelers, and a 24.2% increase in diagnostic yield from metagenomic sequencing, but authors noted integration into routine care remains the principal challenge.
sources:
- peer_reviewed | Journal of Travel Medicine | https://doi.org/10.1093/jtm/taag082 | 2026-09-09
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