Artificial intelligence applications in intracerebral hemorrhage care: implications for clinical and nursing practice - a narrative literature review - Seoyoung Kim; Jungmin Lee; Soo-Hyun Nam (Frontiers in Rehabilitation Sciences)
Little is known about how artificial intelligence tools are utilized across the different stages of intracerebral hemorrhage care or how they contribute to clinical decision-making and patient outcomes in this population. This narrative review aimed to explore current applications of artificial intelligence in the clinical management of patients with intracerebral hemorrhage. A comprehensive search was conducted across five electronic databases (PubMed, CINAHL Plus with Full Text, Ovid MEDLINE, ProQuest, and Web of Science), supplemented by additional manual searches. This review included studies published in English between January 1, 2014, and December 31, 2024. Seven studies examining the application of artificial intelligence in the acute and post-acute phases of intracerebral hemorrhage care were included. In the acute phase, machine learning models such as Random Forest and XGBoost outperform traditional prognostic scoring systems, offering clinicians more precise tools for early risk stratification. In the post-acute phase, AI contributes to continuity of care by supporting data completion, rehabilitation planning, and remote rehabilitation, thereby enhancing patient-centered nursing practice with high predictive accuracy and practical utility. These findings suggest that artificial intelligence holds significant promise for enhancing prognosis prediction, clinical decision-making, and continuity of care in patients with intracerebral hemorrhage.