Algorithmic soft borders: how generative AI reshapes destination competitiveness in medical tourism

Abstract

Generative AI is fundamentally transforming medical tourism by replacing autonomous search with algorithmically curated ecosystems. Drawing on a large-scale algorithmic audit across five leading LLMs, this study theorises the emergence of Algorithmic Soft Borders and the Algorithmic Promise-service narratives that frame pre-arrival cognitive anticipation. Findings reveal that AI systems act as active market architects, prioritising a limited set of global hubs through two exclusionary mechanisms. First, Linguistic Localization creates a local loop effect, structurally narrowing geographic recommendations when communication trust is emphasised. Second, Semantic Bifurcation emerges as native-language prompts systematically prioritise clinical safety, whereas English prompts emphasise cost-efficiency. Crucially, this reveals a narrative asymmetry for high-tier destinations (e.g. the USA), where AI inherently fails to project communication comfort to non-English cohorts, creating hidden market-share risks. Together, these mechanisms generate parallel market realities without altering physical infrastructure. The study concludes that traditional SEO is insufficient, proposing the Large Language Model Optimization (LMO) framework and the Gap Score as critical tools for managers to align algorithmic narratives with operational reality.