インテリジェントニードル：超音波ガイド下区域麻酔における人工知能の役割

The integration of artificial intelligence (AI) into ultrasound-guided regional anesthesia (UGRA) marks a new stage in anesthetic practice. Since ultrasound was first introduced in 1989, UGRA has improved precision and safety, but its success still depends on operator expertise and the ability to interpret complex images. AI, particularly deep learning algorithms, can automatically identify anatomical landmarks, optimize ultrasound settings, and track needle position with high accuracy. These features support anesthesiologists during procedures, shorten the learning curve, and enhance education for trainees. Early studies show that AI-assisted systems improve image quality, increase confidence in sonoanatomy recognition, and provide real-time feedback during both peripheral nerve and fascial plane blocks. Such tools are especially useful in complex scenarios, where fascial layers are difficult to distinguish. However, widespread clinical use remains limited by challenges such as the need for large, diverse datasets, anatomical variations, and ethical considerations around automation. Looking ahead, AI may extend to predictive analytics, estimating anesthetic volume, block success, and even integrating with robotic systems for enhanced precision. The aim is not to replace the anesthesiologist but to augment clinical expertise with computational support. AI-assisted UGRA has the potential to improve safety, efficiency, and training, shaping the future of regional anesthesia.