Influence of emerging technologies applied to the prognosis of traumatic brain injury
Keywords:
Clinical prediction, neurological prognosis, predictive models, clinical decision making, emerging technologiesAbstract
Intracranial traumatic injuries, such as acute subdural hematoma, cerebral contusion, and post-traumatic subarachnoid hemorrhage, have become a public health problem due to their high morbidity and mortality rates and the complexity of their clinical management. Within this context, artificial intelligence has emerged as an innovative alternative to optimize the prediction of clinical severity and support timely medical decision-making. Therefore, this study aimed to analyze the current scientific evidence regarding artificial intelligence models designed to predict clinical severity in patients with acute subdural hematoma, cerebral contusion, and post-traumatic subarachnoid hemorrhage. A descriptive literature review was conducted using international biomedical databases. The review identified that machine learning and learning models have demonstrated high levels of predictive accuracy, particularly in estimating adverse clinical outcomes, risk stratification, and supporting early diagnosis, although methodological variability exists among studies. It was concluded that the analyzed evidence indicated that artificial intelligence has proven to be a promising tool for predicting clinical severity in traumatic brain injury. However, ethical, technical, and operational limitations have persisted, requiring methodological standardization and strengthening of future clinical validation. Therefore, it has been considered that future lines of research should focus on developing explainable models, expanding heterogeneous databases, and the ethical and regulatory integration of these technologies.
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