Artificial Intelligence and Complementary Digital Health Technologies Across the Travel Medicine Continuum: A Narrative Review

Clicks: 22
ID: 328271
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #28 of 64 articles by views in international journal of travel medicine and global health

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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-travel diagnosis and surveillance. Machine learning, clinical decision-support systems, large language models, wearable technologies, telemedicine, outbreak surveillance, precision diagnostics and interoperable digital health infrastructure were reviewed. RESULTS: Machine learning improved individualized risk prediction before travel and supported diagnostic decision-making after travel, while clinical decision-support systems enabled more personalized preventive and therapeutic recommendations. During travel, AI-assisted border screening detected SARS-CoV-2 outbreaks up to nine days earlier than conventional surveillance, and wearable Internet of Things (IoT) technologies enabled continuous physiological monitoring in travelers and mass gatherings. The MALrisk model predicted imported malaria with an area under the receiver operating characteristic curve of 0.98, achieving 100% sensitivity and 72% specificity. In hospitalized returned travelers, ChatGPT-4o identified the correct diagnosis in 68% of patients and included the correct diagnosis among its three leading differential diagnoses in 78%, while metagenomic next-generation sequencing increased the diagnostic yield by 24.2% beyond routine investigations. CONCLUSIONS: AI and complementary digital health technologies have the potential to improve travel healthcare throughout the travel continuum. The principal challenge is no longer the development of individual AI applications, but determining how complementary technologies can be effectively integrated into routine travel medicine. Current evidence remains largely retrospective, highlighting the need for prospective multicenter implementation studies evaluating clinically meaningful outcomes.
Reference Key
openalex_W7212015768 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Perry J. J. van Genderen, Eric N M van Sprang
Journal international journal of travel medicine and global health
Year 2026
DOI
10.1093/jtm/taag082
URL
https://doi.org/10.1093/jtm/taag082
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.