External validation means testing an AI model on truly independent data from other sites, time periods or populations to see if its performance holds up. Stakeholders may mistakenly rely on internal accuracy metrics alone; emphasising external validation clarifies why regulators and health systems demand robust, multi‑setting evidence before trusting AI for high‑stakes clinical or R&D decisions.