{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:05:33Z","timestamp":1790255133847,"version":"4.1.0"},"reference-count":60,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 technologies enabled continuous physiological monitoring in travellers 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 travellers, 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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 multicentre implementation studies evaluating clinically meaningful outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jtm\/taag082","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T11:38:49Z","timestamp":1788867529000},"source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence and complementary digital health technologies across the travel medicine continuum: a narrative review"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9272-752X","authenticated-orcid":false,"given":"Perry J J","family":"van Genderen","sequence":"first","affiliation":[{"name":"Corporate Travel Clinic BV , Expertise Centre Travel Risk Management, Rotterdam ,","place":["The Netherlands"]},{"name":"SkinTwin BV , Rotterdam ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric N M","family":"van Sprang","sequence":"additional","affiliation":[{"name":"SkinTwin BV , Rotterdam ,","place":["The Netherlands"]},{"name":"Department of Analytical Chemistry & Chemometrics , Radboud University, Nijmegen ,","place":["The Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"2026092408315626600_ref1","volume-title":"UN Tourism Data Dashboard","author":"UN Tourism","year":"2026"},{"key":"2026092408315626600_ref2","doi-asserted-by":"publisher","first-page":"1560","DOI":"10.1086\/518173","article-title":"Fever in returned travelers: results from the GeoSentinel surveillance network","volume":"44","author":"Wilson","year":"2007","journal-title":"Clin Infect Dis"},{"key":"2026092408315626600_ref3","doi-asserted-by":"publisher","DOI":"10.1093\/jtm\/tax046","article-title":"What proportion of international travellers acquire a travel-related illness? 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