Artificial Intelegence

The goal of this observational study is to evaluate the feasibility and acceptability of a 12-week intervention utilizing a Fitbit and artificial intelligence (AI)-delivered diabetes self-management education and support (DSMES) with tailored text messages. The main question it aims to answer is: Does providing a wearable fitness and activity tracker plus AI-tailored and DSMES improve clinical outcomes for patients with type 2 diabetes? Participants will complete a baseline visit, wear a Fitbit and answer text messages for 12-weeks, and complete by a final visit.

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.