Artificial intelligence in ultrasound-guided regional anesthesia AI in ultrasound-guided regional anesthesia

Objective: To critically analyze the clinical applications of artificial intelligence (AI) in ultrasound-guided regional anesthesia to elucidate its potential for integration into contemporary and future clinical practice. Methods: A literature review was conducted in PubMed/MEDLINE, Scopus, and Web of Science from 2019 to 2025. Original studies applying AI to segmentation, classification, probe guidance, three-dimensional reconstruction, or needle trajectory planning, reporting either technical metrics or clinical outcomes, were included. Editorials, abstracts without full text, and purely technical studies were excluded. Data extraction was performed in duplicate, and results were synthesized through comparative narrative analysis. Results: In peripheral nerve blocks, algorithms achieved accuracies above 90% in segmentation and classification, leading to improvements in anatomical identification and procedural time reduction, particularly among inexperienced operators. Three-dimensional reconstruction contributed to favorable clinical outcomes in anesthetic parameters and postoperative pain. In neuraxial blocks, automated models achieved success rates exceeding 90% and demonstrated strong correlations with clinical measures, although evidence remains largely restricted to homogeneous populations and experimental validations. Conclusion: AI in ultrasound-guided regional anesthesia demonstrates tangible clinical benefits; however, its consolidation requires multicenter studies that integrate patient-centered outcomes and ethical frameworks ensuring safety, transparency, and equity.