Hangsik Shin

Abstract Large language models (LLMs) show strong generalization, yet their ability to interpret structured medical data remains insufficiently studied. This work evaluated four LLMs—Claude Sonnet 4, Gemini 2.5 Pro, GPT-4o, and LLaMA 3.1-70B—using comprehensive health checkup data from the Korean National Health Insurance Service. Multiple prompting strategies (few-shot, role-based, constraint-based, and Chain-of-Thought) were tested.

Abstract This study developed and evaluated a model for assessing pain during the surgical period using photoplethysmogram data from 242 patients. Pain levels were measured at 2 min intervals using a numerical rating scale or clinical criteria: preoperative, before and after intubation, before and after skin incision, and postoperative. Key features from the photoplethysmography waveform were extracted to build XGBoost-based models for intraoperative and postoperative pain assessment.

Abstract Photoplethysmography (PPG) is a non-invasive optical technique that measures changes in blood volume in the microvascular tissue bed of the body. While it shows potential as a clinical tool for blood pressure (BP) assessment and hypertension management, several sources of error can affect its performance. One such source is the PPG-based algorithm, which can lead to measurement bias and inaccuracy.