Background: Artificial intelligence (AI) and machine learning (ML) have significantly advanced medical diagnostics and surgical procedures, particularly in spinal and cranial surgery. Robotic-assisted surgery has emerged as a transformative approach, offering increased precision, reduced intraoperative complications, and improved surgical outcomes. This review examines the effectiveness and reliability of AI in spinal and cranial diagnosis, its integration into robotic-assisted surgical interventions, and the associated ethical concerns. Method: An extensive literature search was search was conducted on different search engines such as PubMed, Google Scholar, and Scopus to find relevant articles. Result: Findings suggest that AI models exhibit high accuracy in detecting spinal and cranial pathologies. ML algorithms contribute to enhanced prognostic assessments and decision-making in neurosurgery. Robotic-assisted surgeries have superior accuracy, lower radiation exposure, and fewer postoperative complications compared to conventional methods. However, challenges such as data biases, lack of transparency in AI decision-making, regulatory hurdles, and the high costs of AI-driven interventions pose significant barriers to widespread adoption. Ethical concerns, including patient privacy, algorithmic bias, and the potential overreliance on AI, must be addressed to ensure responsible integration into clinical practice. Conclusion: Use of AI and machine learning improves the diagnostic outcomes and decreases post op complications in the field of spinal and cranial surgery. But certain challenges such as ethical concerns and technical hurdles should be sorted out with effective planning. Further research is necessary to refine AI-driven interventions, enhance cost-effectiveness, and to make sure ethical and equitable implementation of AI and robotic surgery in neurosurgical care.

Introduction Traumatic brain injury (TBI) is considered a global health crisis. It results in injury to the brain from an external impact leading to severe disability, with higher incidence statistics recently observed in young adults and elderly individuals. Purpose The review aims to analyze current research findings explaining the prevalence, incidence, and demographics of such injuries stressing the need for enhanced prevention and management techniques. The pathophysiology involves a complex cascade of biochemical and cellular events, which results in necrotic-apoptotic neuronal cell death. Method The review synthesizes current research on TBI, highlighting epidemiological data, advanced diagnostics, and effective rehabilitation strategies like physical and cognitive therapy. Findings Traumatic Brain Injury is an important public health issue with high morbidity and mortality rates causing extreme damage to a person, such as comas, catatonic states for various years, or lifelong injury or demolition. Early diagnostic imaging techniques include CT, MRI, advanced neuroimaging methods, and blood-based biomarkers. In the acute setting, initial management is directed toward stabilizing and preventing secondary injuries and early mobilization. The goals of rehabilitation are to promote the highest level of functional independence and physical function, cognitive functions, and social integration. Conclusion This review highlights that the use of new rehabilitation technologies, such as VR-based and robot-assisted therapies, might lead to improved recovery. Long-term supportive care combined with community integration is mandatory for enhancing quality of life. A systematic multidisciplinary team approach and family support are needed for traumatic brain injury survivors.

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