A Comprehensive Survey on Annotation to Clinical Application: Artificial Intelligence for Intracranial Hemorrhage Detection and Segmentation in Neuroimaging

While artificial intelligence (AI) makes remarkable strides in automating intracranial hemorrhage (ICH) detection, current surveys often focus on narrow aspects of the problem, leaving critical areas such as data labeling, model interpretability, scan-level image fusion, and AI’s comparative performance with radiologists inadequately explored. Additionally, challenges like dataset inconsistency, limited generalization across diverse clinical environments, and integration into real-world workflows remain largely unaddressed, hindering the widespread clinical adoption of AI models for ICH detection.This survey aims to fill these gaps by offering a comprehensive, all-encompassing exploration of AI’s role in ICH detection. We provide an in-depth analysis that spans the entire diagnostic pipeline, from data annotation, preprocessing, and augmentation to model optimization, complexity reduction, and explainable AI (XAI). In addition, we critically assess AI’s performance relative to human radiologists, exploring how AI can augment clinical decision-making rather than replace it. Highlighting advanced scan-level fusion methods like multiple-instance learning and fuzzy decision-making, we present a robust approach for ensuring accurate, reliable, and clinically deployable systems. Through a synthesis of the high-quality studies, this survey not only benchmarks the latest advancements but also identifies key research challenges and future directions necessary to accelerate the clinical integration of AI in ICH detection.