Tehran University of Medical Sciences Nursing Practice Today 2383-1154 12 2 2025 03 15 141 159 Chia-Lun Lo Department of Health-Business Administration, Fooyin University, Kaohsiung, Taiwan Chia-En Liu Department of Nursing, St Joseph’s Hospital, Yunlin, Taiwan Hsiao Yun Chang Department of Nursing, Chang Gung University of Science and Technology, Taoyuan, Taiwan AND Division of Endocrinology and Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan Chiu-Hsiang Wu Department of Nursing, Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan 2024 07 23 2025 03 03 Background & Aim: Falls among hospitalized patients pose severe consequences, necessitating accurate risk prediction. Traditional assessment tools rely on cross-sectional data and lack dynamic analysis, limiting clinical applicability. This study developed an AI-based fall risk prediction model using supervised learning techniques to enhance predictive accuracy and clinical integration. Methods & Materials: This study was conducted at a medical center in Taiwan, excluding pediatric patients due to non-disease-related fall factors. Fall cases were obtained from hospital records, and non-fall cases were stratified based on age and gender to create a balanced 1:1 dataset. A total of 52 predictive variables were identified and refined to 39 through expert review. The AI model was compared with MORSE, STRATIFY, and HII-FRM using supervised learning with 10-fold cross-validation. Performance was evaluated based on accuracy, sensitivity, and specificity. Results: The results demonstrated that the AI-based model significantly outperformed traditional fall risk assessment tools in accuracy, sensitivity, and specificity. More importantly, the model’s superior predictive power allows for real-time risk assessment and seamless integration into clinical decision support systems. This integration can enable timely interventions, optimize patient safety protocols, and ultimately reduce fall-related incidents in hospitalized settings. Conclusion: By automating risk assessment, the AI model can alleviate the workload of healthcare professionals, reducing the time required for manual evaluations and minimizing subjective biases in clinical decision-making. This not only enhances operational efficiency but also allows nursing staff to allocate more time to direct patient care. These findings underscore the transformative potential of AI-driven approaches in healthcare, improving patient safety through data-driven. https://npt.tums.ac.ir/index.php/npt/article/view/3374 https://npt.tums.ac.ir/index.php/npt/article/download/3374/648