Artificial Intelligence Tools in Pre-Travel Health Consultations: A Scoping Review of Clinical Evidence, Implementation Gaps, and Emerging Opportunities
Abstract
Background: 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retrieval, multilingual communication and post-consultation reinforcement, but unsafe use may introduce hallucinated, outdated or insufficiently personalised recommendations.
Objectives: 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.
Methods: A scoping review was conducted following PRISMA-ScR reporting, using a…
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