Artificial Intelligence in Intracerebral Hemorrhage: Current Applications and Future Perspectives

Intracerebral hemorrhage (ICH) remains one of the most severe forms of stroke and is associated with high mortality, poor functional outcomes, and substantial healthcare burden worldwide. Despite advances in neurocritical care and minimally invasive surgical techniques, the management of ICH remains challenging because of disease heterogeneity, rapid neurological deterioration, and the lack of effective individualized treatment strategies. In recent years, artificial intelligence (AI) has emerged as a promising tool for improving the diagnosis, prognostic evaluation, and precision management of ICH. A systematic literature search was performed in PubMed, Web of Science, and Embase to identify studies on AI applications in ICH, with predefined inclusion criteria focusing on imaging analysis, prognostic prediction, clinical decision support, and minimally invasive surgery. This review summarizes the major clinical applications, limitations, and future directions of AI in ICH, including multimodal foundation models, intelligent surgical assistance, and personalized precision care. Recent studies have demonstrated that AI-based models can significantly improve the accuracy of hematoma segmentation, hematoma expansion prediction, and functional outcome prognostication compared with conventional approaches. Despite encouraging progress, several important barriers continue to limit clinical translation, including data heterogeneity, limited external validation, insufficient interpretability, ethical and regulatory concerns, and challenges in workflow integration. Overall, AI has the potential to transform ICH management from conventional experience-based practice to data-driven, personalized, and precision neurosurgical care.