AI-Agent for Automated Diagnosis and Predicting Using EHR and Multimodal Data

Condition(s) studied

Study summary

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.

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).
• F1 Score — 1 year
  The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.

Trial sites (6)

More The Eye Hospital of Wenzhou Medical University trials in China

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