부산대학교 도서관

Objectives: To examine the current state of fall prevention by the integration artificial intelligence (AI) technology and deploying 'intelligent fall prevention lighting and alert systems in healthcare settings.' Methods: This study developed an AI algorithm for human skeletal image recognition and established a workflow for bed-exit alert software, which was thereafter incorporated into a holistic AI-based fall prevention framework for clinical validation. Results: The project successfully developed an AI algorithm for human skeletal image recognition and established a workflow for the bed-exit warning software. The preliminary validation of the fall prevention lighting system achieved a detection accuracy rate of up to 90%. Conclusions: The application of AI to bed-exit alerts and patient recognition is an innovative step toward fall prevention strategies. The use of AI technology in clinical care settings is an anticipated future trend with the potential to improve the quality of care. However, as AI technology rapidly evolves, the continuous validation and enhancement of accuracy in complex clinical care environments remain challenging and require ongoing effort.