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.

This study aims to build and apply an artificial intelligence (AI) model to assist doctors in predicting patient diagnoses and outcomes, such as survival or hospital stay length. Patients will be enrolled across the hospital, and real-time, multimodal health data-including lab results, vital signs, medical history, and imaging-from electronic health records will be used. The study will follow participants to evaluate the AI model's performance against standard practice. The goal is to improve the accuracy and speed of diagnoses and treatments, enhancing patient care. This prospective study tests the model in real-world hospital settings.

Inclusion Criteria:

1. Patients admitted to any department of the hospital (e.g., ICU, general wards, emergency, outpatient services) during the study period.
2. Patients with available real-time electronic health record (EHR) data, including at least two of the following: laboratory results, vital signs, medical history, and imaging data.

Exclusion Criteria:

Patients currently enrolled in another clinical trial that could interfere with data collection or outcomes of this study.

This study includes all patients admitted to any hospital department, with real-time electronic health record data (e.g., labs, vital signs, history, imaging). Participants must consent to data collection.