Artificial Intelligence Tools in Pre-Travel Health Consultations: A Scoping Review of Clinical Evidence,...

Pre-travel health consultations require individualised risk assessment across itinerary, destination, traveller characteristics, vaccine and medication history, comorbidities, pregnancy and immune status, activities, and access to care. Artificial intelligence (AI), particularly large language models (LLMs), may support pre-consultation education, structured history collection, guideline retrieval, multilingual communication and post-consultation reinforcement, but unsafe use may introduce hallucinated, outdated or insufficiently personalised recommendations.

This scoping review maps the current evidence on AI tools relevant to pre-travel health consultations, characterises implementation gaps, identifies patient-safety risks and proposes a supervised implementation model for travel medicine clinics. Original contribution: Unlike previous reviews of clinical AI, patient-education LLMs or chatbots in chronic illness, this is the first scoping review focused specifically on AI in pre-travel consultations. It uniquely combines a five-tier evidence hierarchy that separates direct travel-medicine AI evidence from indirect clinical-AI safety and equity evidence, and provides a travel-medicine-specific clinical safety risk taxonomy and a supervised implementation framework anchored to authoritative travel-medicine guidance and current AI regulatory regimes.

A scoping review was conducted following PRISMA-ScR reporting, using a Population-Concept-Context eligibility framework and a targeted retrieval in May 2026 covering January 2017 to May 2026. Sources were screened and charted by a single reviewer using a structured eligibility checklist. Quality and applicability were appraised conceptually using MMAT, AMSTAR 2 and JBI text-and-opinion criteria, with GRADE-informed certainty.

Of 70 records identified, 11 were included: four direct pre-travel AI sources, one adjacent travel-related decision-support study, four guideline and context sources and two clinical LLM safety sources. The only patient-level implementation involved 26 travellers using a GPT-4 Travel Clinic Assistant in Singapore, where physicians and travellers reported acceptability and workflow benefit but objective effectiveness outcomes were not measured. Broader clinical LLM evidence indicates heterogeneous evaluation methods, vulnerability to hallucinated guidelines, and accuracy that varies widely across model versions and specialties.

Current evidence supports supervised AI augmentation of pre-travel consultations but does not support autonomous AI-led vaccine selection, malaria prophylaxis, contraindication screening or individualised travel-risk clearance. Near-term deployment should be restricted to clinician-supervised education, structured intake, source-grounded guideline retrieval, after-visit reinforcement and escalation-triggered workflow support. Priority research includes travel-medicine-specific hallucination audits; equity testing in visiting-friends-and-relatives, migrant, older-adult, First Nations Australian, and Pacific Islander travellers; and prospective trials reported under CONSORT-AI, SPIRIT-AI and TRIPOD + AI.