The AI Clinical Innovation Lab at UCSF focuses on developing advanced machine learning models to predict and prevent adverse outcomes in the perioperative setting. Our research aims to integrate these models into clinical workflows to provide real-time risk assessment and prediction during surgeries. We are dedicated to identifying new modifiable risk factors and treatments for acute kidney injury (AKI) and other adverse outcomes, validating our models for clinical use, and eventually seeking FDA approval. We also design and develop other medical devices in the space of pain prediction and reduction and as well as robotic tools to improve procedural safety and efficiency. Our goal is to enhance patient outcomes and safety through innovative AI solutions. Our current research projects include: Preoperative AKI Prediction Model: Developing and validating a model to predict the risk of AKI before surgery based on patient history and preoperative data. Intraoperative AKI Prediction Model: Creating a real-time model to assess AKI risk during surgery using intraoperative data. High-Frequency Medical Data Collection: Implementing systems to gather and analyze high-frequency medical data during procedures like intubation to improve patient monitoring and outcomes. Identification of Modifiable Risk Factors: Researching new modifiable risk factors for AKI to develop targeted interventions. Medical Devices for Anesthesia Procedures: Using machine learning and AI techniques to the improve the safety and efficiency of medical procedures like intubation.
