Performance Comparison of Large Language Models in TAP Block Ultrasound Interpretation

The goal of this study is to learn how accurately two artificial intelligence (AI) models, Gemini 2.5 Pro and ChatGPT-5.1, can interpret ultrasound videos of the Transversus Abdominis Plane (TAP) block, a regional anesthesia technique used for pain control after surgery. The main questions this study aims to answer are: How accurately can each AI model identify anatomical structures on TAP block ultrasound videos? Can the AI models correctly evaluate the spread of local anesthetic and determine whether the block is successful? How closely do the AI models' answers match the evaluations of expert anesthesiologists? No additional procedures will be performed on patients. TAP blocks will be done as part of routine clinical care, and the ultrasound videos will be recorded and de-identified. Participants will not need to do anything extra for the study. Experienced anesthesiologists will review the videos and provide expert answers. The AI models will be given the same videos and asked the same questions. A second expert, who does not know which answers came from humans or AI, will compare all responses. The results will help researchers understand whether advanced AI systems can safely support clinicians in interpreting ultrasound-guided regional anesthesia procedures and improve education and decision-making in anesthesia practice.

NCT Number	NCT07261436
Lead Sponsor	Kanuni Sultan Suleyman Training and Research Hospital
Conditions	Artificial Intelegence
Enrollment	40 participants
Start Date	2026-01-15
Primary Completion	2026-04-30 (estimated)
Study Completion	2026-05-01 (estimated)
Updated on ClinicalTrials.gov	2026-02-04