Performance Comparison of Large Language Models in TAP Block Ultrasound Interpretation: A Double-Blind Prospective Study
Performance Comparison of Large Language Models in TAP Block Ultrasound Interpretation: A Double-Blind Prospective Study
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.
This study aims to evaluate how two advanced artificial intelligence (AI) models, Gemini 2.5 Pro and ChatGPT-5.1, interpret ultrasound videos of Transversus Abdominis Plane (TAP) block procedures. TAP blocks are performed as part of routine clinical care by experienced anesthesiologists. The ultrasound videos recorded during these procedures serve as the data source for this study. No additional procedures or patient involvement are required beyond standard care.
Ultrasound Video Processing All ultrasound recordings will be fully de-identified by removing patient names, dates, and any other identifying information.
Gemini 2.5 Pro will receive original video files. ChatGPT-5.1 will receive high-resolution GIF segments generated from the same recordings.
Both models will be given identical structured prompts consisting of eight clinically relevant questions about anatomic structures, needle placement, local anesthetic spread, dermatomal effects, and potential safety concerns.
Expert Participation
Two anesthesiology experts will participate independently:
Expert A will review each ultrasound video and answer the same set of eight clinical questions. These answers will serve as the primary human clinical reference.
Expert B will independently evaluate all responses, those from Expert A, Gemini, and ChatGPT-5.1, after they have been anonymized and randomly ordered. Expert B will not know whether a response originated from an AI model or a human expert. Expert B will assess anatomical accuracy, clarity, clinical appropriateness, and overall content quality for each answer.
If Expert A and Expert B disagree on the interpretation or quality assessment of any response, a third expert (Expert C), who is also experienced in ultrasound-guided regional anesthesia, will independently review the relevant responses. Expert C's evaluation will be used to resolve discrepancies and establish the final consensus.
Data Collected
For each TAP block video, the following information will be recorded:
Ultrasound and procedural details. Patient demographic descriptors (age, sex, BMI, ASA classification), used only for general characterization of the sample.
AI-related performance features such as response completeness, relevance, confidence level, and response time.
Inclusion Criteria:
Exclusion Criteria:
enginihsan@hotmail.com+905382431114