Role of AI and Nanotechnology in USG-Guided TAP Blocks and Pain Management:
Year: 2025
Type: article
Abstract: The increasing demand for effective pain management techniques in clinical practice has led to the exploration of advanced technologies such as Artificial Intelligence (AI) and Nanotechnology. Pain management, particularly postoperative pain management, is a critical aspect of patient care in surgery and various medical procedures. Among various regional anesthesia techniques, the Transversus Abdominis Plane (TAP) block well-established regional anesthesia technique used for managing abdominal pain and is a key method for perioperative analgesia, particularly in abdominal surgeries. Ultrasound guidance (USG) has revolutionized this approach by providing real-time visualization, enhancing the accuracy of needle placement and reducing complications. With the advent of Ultrasound-Guided (USG) TAP blocks, the precision and success of this technique have dramatically improved. Despite its effectiveness, the TAP block still has room for improvement in terms of efficacy, precision, and patient safety. Emerging technologies like Artificial Intelligence (AI) and Nanotechnology offer promising solutions to optimize the TAP block's performance. AI can enhance decision-making and procedural accuracy, while Nanotechnology can improve the delivery and duration of analgesic agents. The integration of AI and Nanotechnology has revolutionized TAP block procedures, optimizing both the performance and outcomes. AI algorithms assist in real-time imaging and needle guidance, while Nanotechnology offers the potential for controlled drug release, reducing side effects and improving analgesia duration. This paper aims to review the integration of AI and Nanotechnology into USG-guided TAP blocks, their clinical impact, and their potential for revolutionizing pain management in the future.
Authors: Mamta Sannurkar -, Satish Gaikwad -
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4407868217