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Background and Aims Artificial intelligence (A.I.) is applied now as an integral part of our day-to-day life and AI and robotics in regional anesthesia(RA) has brought about transformative changes in acute pain management for surgical procedures(1). RA has traditionally been performed using anatomical landmarks to identify underlying structures, Now Sonoanatomy by Ultrasound (USG). The ability to acquire and interpret optimal sonographic images requires many years of training, and remains a barrier to successful delivery of US guided Regional anesthesia (UGRA)(2). Use of AI can help in trainees’ education, understanding, easy applicability and improve the success rate in novice.

Methods We conducted a literature search via PubMed, Scopus, and Google Scholar, using the following keywords: artificial intelligence, robotics, technology, regional anesthesia, ultrasound (US)-guided nerve block, Education and Training in last 7 years.

Results ScanNavTm, Deep learning, needle tracking and outcome AI utility in Tranee for USGRA. Scan Nav(3) shows non-experts were more likely to provide positive and less likely to provide negative feedback than experts (p=0.001). Experts, it was for its utility in teaching (30/60, 50%). Real-time and remote experts reported a potentially increased risk in 12/254 vs 8/254 (p=0.362) scans, respectively. AI was reported to be helpful in 99.7% of the cases(4).

Conclusions AI-guided USG-RA can enhance the optimisation, interpretation of the sonographic image, visualisation of needle advancement and injection. AI-guided USG RA models might improve the training process among residents trainee(4). More high-quality studies are warranted to generate evidence of AI-guided USG-RA in different patient populations, anatomical regions, nerve blocks and errors while using it.