In this narrative review, we explore the evolving role of machine learning (ML) in the diagnosis, prognosis, and clinical management of traumatic brain injury (TBI). The increasing prevalence of TBI necessitates advanced techniques for timely and accurate diagnosis, and ML offers promising tools to meet this challenge. Current research predominantly focuses on integrating clinical data, patient demographics, lab results, and imaging findings, but there remains a gap in fully harnessing the potential of image features. While advancements have been made in areas such as subdural hematoma segmentation and prognosis prediction, the translation of these techniques into clinical practice is still in its infancy. This is further compounded by challenges related to data privacy, clinician trust, and the interoperability of various health systems. Despite these hurdles, FDA-approved ML applications for TBI and their subsequent promising results underscore the potential of ML in revolutionizing TBI care. This review concludes by emphasizing the importance of bridging the gap between theoretical research and real-world clinical application and the necessity of addressing the ethical and privacy implications of integrating ML into healthcare.

A woman in her 70s presented to primary care clinic complaining of acute onset dizziness for 1 day that was initially diagnosed as vestibular neuritis and treated with steroids. The next day, she presented to the emergency department with worsening symptoms. Imaging revealed no intracranial process; however, non-contrast CT imaging revealed a soft-tissue mass in the posterior ethmoid sinus. The vertigo completely resolved before an otolaryngologist surgically removed the nasal mass, which actually originated from the right cribriform plate and extended to the anterior middle turbinate head. The final pathology was consistent with seromucinous hamartoma.