Collaborative Artificial Intelligence Integration in the Management of Cleft Lip and Palate Patients: Current State of the Art

Cleft lip and/or palate (CLP) involves a multidisciplinary and longitudinal care paradigm that provides a rational climate within which collaborative AI systems can buttress clinical decision-making. This study seeks to summarize how Artificial Intelligence AI has been integrated into CLP management by reviewing relevant publications over the past decade while focusing on the deployment of AI into CLP care along the entire care continuum. A pre-existing systematic review analyzing AI in children with CLP was a Foundational Evidence for the study and narratively updated with more recent pediatric craniofacial and orthognathic literature involving CLP subgroups. Other eligible studies had to have leveraged AI or machine learning for CLP-related tasks including diagnosis, landmarking, segmentation, surgical prediction, presurgical orthopedics, or functional outcome evaluation. The results are summarized by clinical domain. Twelve CLP-preferential studies from the prior systematic review, with a number of related craniofacial studies were reported. Across the domains, models tended to record clinically acceptable errors or good classification, reduced clinician’s manual workload, and provided standardized (and readily shareable) outcomes for multidisciplinary participation. The majority of publications were retrospective and single-center with small sample sizes and scant external validation. Collaborative AI in CLP care has advanced to early clinical workflows along the care continuum, essentially augmenting clinician’s expertise. Equity, ethics, and scalability of implementation will require robust multicenter validation, diverse datasets, governance frameworks, and clinician AI literacy