Objective:

To summarize recent advances in AI applications for perioperative risk stratification and complication management in craniomaxillofacial surgery.

Approach:

• Preoperative Management: Focus on intelligent early recognition of syndromic craniosynostosis to improve individualized treatment protocols.
• Intraoperative Support: AI provides decision support and safety monitoring during surgical procedures.
• Postoperative Management: AI aids in risk stratification and quantification of outcomes after surgery.

Key Findings:

• AI enhances diagnostic accuracy and risk stratification in craniomaxillofacial surgery.
• Current research faces challenges in model interpretability, external validation, and clinical integration.
• Future directions include multimodal AI, explainable AI, and generative AI for personalized perioperative management.

Interpretation:

Limitations:

• Challenges in data quality and generalizability.
• Issues with model interpretability and clinical trust.
• Barriers to clinical translation and implementation.

Conclusion:

The review serves as a structured reference for the intelligent development of perioperative care in craniomaxillofacial surgery.