Intracranial Hemorrhage Detection

Machine learning (ML) has significant potential to transform healthcare, particularly in interpreting medical imaging. However, many studies evaluate ML models in controlled settings, highlighting the need for real-world validation to ensure clinical applicability. This study demonstrates that ML model generalizability is achievable in medical imaging, using the detection of intracranial hemorrhage on non-contrast CT scans as a case study, with the model showing strong performance in both controlled and real-world external validation datasets.