Artificial Intelligence in the Management of Facial Palsy: A Narrative Review Across the Care Continuum.

Introduction: Facial paralysis produces substantial functional and psychosocial impairment which facial reanimation surgery seeks to mitigate by restoring facial movement and improving symmetry. However, despite technical advances in nerve- and muscle-based reconstruction, perioperative evaluation and outcome assessment remain largely subjective and variably reproducible. Artificial intelligence (AI) and computer vision have emerged as tools to provide objective, scalable assessment across the facial reanimation care pathway. Methods: MEDLINE, Embase, and the Cochrane Library were searched extensively to identify studies evaluating AI applications relevant to facial reanimation surgery. Included studies were screened by three independent reviewers and mapped to stages of perioperative care using an evidence-mapping framework. Results: Thirteen studies met inclusion criteria and were distributed across multiple stages of care. Evidence was most concentrated in diagnosis and severity assessment and in postoperative follow-up, where AI systems automated facial grading scales, quantified asymmetry, and extracted dynamic motion features from photographs and video. Several machine learning and deep learning models demonstrated high diagnostic accuracy and strong correlations with clinician-assigned scores, often exceeding inter-rater reliability of traditional grading systems. Fewer studies addressed prognostic modeling, preoperative planning, or rehabilitation, though emerging applications included outcome prediction, three-dimensional symmetry analysis, and AI-assisted evaluation of spontaneous emotional expression after reanimation. Conclusion: AI applications in facial paralysis care are most mature in assessment and outcome measurement, where they offer improved objectivity and reproducibility. However, broader clinical integration will require larger, diverse datasets, prospective validation, and alignment with ethical and regulatory standards to demonstrate meaningful patient centered benefit.