Artificial Intelligence Tools in Pre-Travel Health Consultations: A Scoping Review of Clinical Evidence, Implementation Gaps, and Emerging Opportunities

Background Pre-travel health consultations require individualised risk assessment across itinerary, destination epidemiology, traveller characteristics, vaccine history, comorbidities, medication profile, pregnancy status, immune status, activities, timing, and access to care [8,10,11]. 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retrieval, multilingual communication, and post-consultation reinforcement, but unsafe use may introduce hallucinated, outdated, or insufficiently personalised recommendations [5,6,14,15]. 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 [1,28-30]. Methods The review was conducted as a scoping review using the Arksey and O'Malley framework as advanced by Levac and colleagues and operationalised through the JBI scoping review guidance, with reporting aligned to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) [1,28-30]. The review was not prospectively registered. Eligibility was defined by a Population–Concept–Context (PCC) framework. Targeted retrieval was conducted in May 2026 through PubMed/MEDLINE (one direct search string), academic and web-indexed search tools, citation chasing from Journal of Travel Medicine and Travel Medicine and Infectious Disease, and authoritative guideline and regulator websites. The search date range was January 2017 to May 2026. Sources were eligible if they addressed AI or digital decision support in pre-travel health, travel medicine, travel-related clinical decision support, clinical LLM safety, or guidance defining the standard pre-travel consultation. Screening and data charting were conducted by a single reviewer using a structured eligibility checklist (Supplement S2). Results Seventy records were identified, one duplicate was removed, 69 records were screened, 12 reports were sought for retrieval, one record could not be retrieved within the search window, and 11 reports were assessed in full text and included in the synthesis. Included sources comprised four direct pre-travel AI sources, one travel-related decision-support study, four guideline and context sources, and two clinical LLM safety sources. Direct evidence is thin: the only patient-level implementation report involved 26 travellers using a GPT-4 Travel Clinic Assistant in a Singapore tertiary travel clinic, where physicians and travellers reported acceptability and workflow benefit but objective effectiveness outcomes were not measured [3]. A ChatGPT pre-travel advice evaluation found generally readable and comprehensive answers to common questions, but responses lacked sufficient personalisation to itinerary, comorbidity, vaccine history, and cost considerations [2]. Broader clinical LLM evidence indicates that evaluation methods remain heterogeneous and that LLMs may repeat or elaborate false clinical details and hallucinate clinical guidelines in simulated decision-support tasks [13,14,16]. Conclusions 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 [2-6,14,15,48,50]. Near-term deployment should be restricted to clinician-supervised education, structured intake, source-grounded guideline retrieval, after-visit reinforcement, and escalation-triggered workflow support [4,5,34,49]. Travel medicine specialists, clinic leaders, regulators, and digital health developers should prioritise domain-specific hallucination audits, equity testing across 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 standards [31-33,37,38,41-44].