A multicenter effort evaluated an adult-trained AI model for newborn chest X-rays by enabling continual learning across 30 neonatal intensive care units without sharing patient data. The work is presented as evidence that model adaptation can occur via local updates while keeping data within hospital boundaries. The key development is the operational framework: the AI model can recalibrate to site-specific imaging characteristics over time, with the promise of more robust performance across equipment and protocols. The approach also aligns with broader regulatory and privacy requirements that increasingly restrict cross-site patient data movement. For NICU-focused stakeholders, the implication is that AI deployment can be paired with iterative learning that may reduce performance drift when patient populations or imaging workflows change. However, external validation and monitoring for safety or bias remain central requirements before clinical-scale implementation.