When is travel-health AI inference transportable? Mobility-conditioned admissibility for cross-border disease risk and decision support

BACKGROUND: Artificial intelligence is increasingly used for infectious disease surveillance, traveller risk assessment, outbreak forecasting and decision support. Travel creates a distinctive transportability problem because people move between epidemiological, geographic, climatic and healthcare contexts in which the conditions supporting an inference may change rapidly. METHODS: This conceptual/theoretical methodological-development study was informed by a targeted narrative review of travel medicine, infectious disease surveillance, human mobility, border health, mass gatherings, antimicrobial resistance and health-AI validation literature. RESULTS: We develop a mobility-conditioned admissibility framework that treats travel as movement across origin, transit, destination and return regimes. Interpretation is conditioned on mobility state, epidemiological regime, exposure history, time-dependent constraints and clinical or biological constraints. Five proposed operational states are retained: supported inference, conditionally transportable inference, active monitoring, restricted interpretation and withholding. The framework also distinguishes low probability from infeasibility and requires preservation of rare but epidemiologically feasible events. CONCLUSIONS: Model availability and conventional predictive validity do not establish inferential transportability across changing travel contexts. Mobility-conditioned admissibility provides a complementary use-boundary layer for travel health AI by making cross-regime support explicit before outputs are interpreted or released. Its incremental value requires prospective evaluation against calibrated uncertainty, external validation and dataset-shift monitoring.