Introduction <p>The contribution and future of artificial intelligence (AI) in cleft lip and/or palate (CL/P) need exploration. The aim of this scoping review is to collate the literature from the last 10 years on the integration of AI in CL/P surgeries and to provide a novel tool based on the level of evidence and regulatory approval for maxillofacial surgeons in practical decision making.</p> Methods <p>A literature search was performed via the PubMed, Science Direct and Cochrane databases from 2015 to 2025, from which 273 articles were identified. After removing duplicate articles and applying the eligibility criteria, 35 articles were included in the review.</p> Results <p>Experimental studies were the most common, followed by observational studies and systematic and narrative reviews. Compared with machine learning models, deep learning AI models are used more often in studies. The studies were assessed on the basis of contribution of AI in relation to CL/P in 4 categories, namely, comprehensive management, detection and diagnosis, treatment and outcome and education. Maximum models were emerging AI, followed by experimental AI with only one model being truly clinically validated. A novel workflow table has been proposed.</p> Conclusion <p>The escalating integration of AI in cleft surgeries demonstrates promising advancements but with limitation to be used only as a support system in surgical decision making. Since it is a prevailing topic, a gap in the literature exists. Future research should aim to develop AI models that are more comprehensible for cleft surgeries with economical technological solutions tailored to contexts with limitations.</p>

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Contribution of artificial intelligence in the management of cleft surgeries: a scoping review

  • Preksha Dubey,
  • Alok Ranjan

摘要

Introduction

The contribution and future of artificial intelligence (AI) in cleft lip and/or palate (CL/P) need exploration. The aim of this scoping review is to collate the literature from the last 10 years on the integration of AI in CL/P surgeries and to provide a novel tool based on the level of evidence and regulatory approval for maxillofacial surgeons in practical decision making.

Methods

A literature search was performed via the PubMed, Science Direct and Cochrane databases from 2015 to 2025, from which 273 articles were identified. After removing duplicate articles and applying the eligibility criteria, 35 articles were included in the review.

Results

Experimental studies were the most common, followed by observational studies and systematic and narrative reviews. Compared with machine learning models, deep learning AI models are used more often in studies. The studies were assessed on the basis of contribution of AI in relation to CL/P in 4 categories, namely, comprehensive management, detection and diagnosis, treatment and outcome and education. Maximum models were emerging AI, followed by experimental AI with only one model being truly clinically validated. A novel workflow table has been proposed.

Conclusion

The escalating integration of AI in cleft surgeries demonstrates promising advancements but with limitation to be used only as a support system in surgical decision making. Since it is a prevailing topic, a gap in the literature exists. Future research should aim to develop AI models that are more comprehensible for cleft surgeries with economical technological solutions tailored to contexts with limitations.