AI-Assisted Medical Decision-Making

Condition(s) studied

Investigational drug(s) / intervention(s)

AI-associated strategy: The intervention in this study involves an AI system that leverages multimodal data fusion to support the clinical decision-making and evaluation of diseases. Patients in this cohort will undergo standard examinations, with clinical decisions guided by the recommendations generated by the AI system.

Study summary

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.

Eligibility

Primary outcome measure(s)

• Area Under the Curve (AUC) — 1 year
  AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
• Overall Hospital Resource Utilization Improvement — 1 year
  The percentage reduction in overall hospital resource use (e.g., bed days, ICU admissions, diagnostic tests) attributed to AI-assisted decision-making, expressed as a percentage.
• Population-Level Diagnostic Accuracy Enhancement — 1 year
  The overall improvement in diagnostic accuracy across all hospital patients (e.g., percentage of correct diagnoses or reduction in misdiagnoses) facilitated by the AI model, expressed as a percentage or ratio.
• System-Wide Reduction in Adverse Event Rates — 1 year
  The percentage reduction in major adverse events (e.g., mortality, severe complications, or prolonged stays) across all hospital patients due to AI-assisted decision-making, expressed as a percentage.

Trial sites (2)

More The Eye Hospital of Wenzhou Medical University trials in China