Structural barriers and pathways to artificial intelligence integration in plastic surgery

Plastic surgery stands at an inflection point: proofs-of-concept for artificial intelligence (AI) abound, yet responsible clinical integration remains limited. We argue this reflects an organizational readiness gap rather than a modeling deficit. Three modifiable barriers dominate: (i) limited AI literacy and fragmented clinician-data-science collaboration; (ii) an immature human-capital pipeline in which junior champions lack senior sponsorship and protected time; and (iii) misaligned incentives and scarce funding that undervalue data sets, code, and rigorous external validation. We propose a pragmatic agenda: embed AI literacy across training and CME; formalize partnerships with engineering/informatics via co-appointments and governance; set aside 4-8 h per week of protected time for data preparation, privacy, legal approvals, model deployment and monitoring; and enable multi-institution studies using privacy-preserving methods (e.g., federated learning). Aligning editorial and funding expectations to reward transparency, calibration, and negative findings can shift the field from one-off prototypes to accountable, generalizable tools that measurably improve patient care.