Clinical trials

This is a multi-center, prospective clinical study designed to evaluate the application and effectiveness of an AI-assisted medical decision support system, leveraging multimodal data fusion, in ophthalmic clinical practice.

This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing cancer, leveraging multimodal health data.

This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing infection, leveraging multimodal health data.

This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted medical decision support system, leveraging multimodal data fusion, in ophthalmic clinical practice.

This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for outcome of dialysis patients, leveraging multimodal health data.

The goal of this clinical study is to explore the potential of using electronic health records (EHR) and multimodal data (such as imaging, lab results, and clinical history) to predict a patient's genotype. The study will evaluate whether predictive models based on this non-genetic data can accurately infer genetic information, which traditionally requires direct genetic testing.

This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying maternal and neonatal diseases, leveraging multimodal health data.

The goal of this clinical study is to evaluate the effectiveness of an AI agent in diagnosing and predicting diseases using electronic health records (EHR) and multimodal imaging data. The AI agent leverages advanced machine learning algorithms to process and analyze diverse health data sources, aiming to assist healthcare providers in making more accurate diagnoses and predictions.

This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). The study will analyze various health data points, including medical history, laboratory results, and clinical observations, to estimate the biological age of patients. By comparing biological age with chronological age, the study aims to assess the accuracy of the model and its potential in identifying age-related health risks and improving patient care.

The study builds and applies an AI model to help doctors predict patient diagnoses and outcomes, such as survival or hospital stay. Real-time, multimodal data (labs, vital signs, history, imaging) from hospital records will be used. Patients will be tracked to compare the AI's performance with standard care. The goal is to improve diagnosis and treatment accuracy in a real-world, prospective study.