AI-Powered Endotracheal Suctioning Training: Improve Patient Safety

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• The proposed LLM-based approach for endotracheal suctioning activity recognition outperforms baseline models by approximately 15-20% in accuracy and F1 score.result 90%
• The framework uses a Large Language Model as the central reasoning module for spatiotemporal activity recognition and explainable decision analysis from video data.method 95%
• The LLM in the framework can verbalize feedback in natural language to translate technical insights into human-understandable guidance for trainees.technical 90%
• The framework incorporates a pilot student-support module built upon anomaly detection and explainable AI (XAI) principles.method 85%
• The student-support module provides automated, interpretable feedback highlighting correct actions and suggesting targeted improvements.technical 85%
• Automated recognition and feedback systems for endotracheal suctioning training remain underexplored despite the procedure's critical importance.market 80%
• Endotracheal suctioning requires a high degree of skill to minimize patient risk, particularly in home care and educational settings where supervision may be limited.market 90%
• The proposed framework establishes a scalable, interpretable, and data-driven foundation for advancing nursing education and improving patient safety.result 80%

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