Artificial intelligence (AI) is rapidly transforming anesthesiology practice across perioperative settings. This review explores the evolution and implementation of hybrid decision-making models that integrate AI capabilities with human clinical expertise. From historical foundations to current applications, we examine how machine learning algorithms, deep learning networks, and big data analytics are enhancing anesthetic care. Key applications include perioperative risk prediction, AI-assisted patient education, automated analysis of clinical records, airway management support, predictive hemodynamic monitoring, closed-loop anesthetic delivery systems, and pain management optimization. In procedural contexts, AI demonstrates promising utility in regional anesthesia through anatomical structure identification and needle navigation, monitoring anesthetic depth via EEG analysis, and improving quality control in endoscopic sedation. Educational applications include intelligent simulators for procedural training and academic productivity tools. Despite significant advances, implementation challenges persist, including algorithmic bias, data security concerns, clinical validation requirements, and ethical considerations regarding AI-generated content. The optimal integration model emphasizes a complementary approach where AI augments rather than replaces clinical judgment—combining computational efficiency with the irreplaceable contextual understanding and ethical reasoning of the anesthesiologist. This hybrid paradigm reinforces the anesthesiologist’s leadership role in perioperative care while enhancing safety, precision, and efficiency through technological innovation. As AI integration advances, continued emphasis on algorithmic transparency, rigorous clinical validation, and human oversight remains essential to ensure that these technologies enhance rather than compromise patient-centered anesthetic care.

One potential complication in bariatric surgery is rhabdomyolysis, which is a condition involving muscle tissue damage that can significantly impact a patient’s health. The causes of rhabdomyolysis can be broadly classified into two major categories: traumatic and non-traumatic. Early investigations into the development of intraoperative rhabdomyolysis in bariatric surgery identified the main risk factors as tissue compression—primarily affecting the lower extremities, gluteal muscles, and lumbar region—as well as prolonged periods of immobilization. Clinically, rhabdomyolysis is typically suspected when a patient presents with muscle pain, weakness, and potentially dark urine or even anuria. However, the most reliable biomarker for rhabdomyolysis is elevated serum creatine kinase levels. The primary goal in managing hydration is to correct intravascular volume depletion, with solutions such as Lactated Ringer’s or 0.9% saline being appropriate options for resuscitation. Perioperative diagnosis of rhabdomyolysis poses a significant challenge for anesthesiologists, requiring a high degree of clinical suspicion, particularly in bariatric patients. In this vulnerable population, prevention is crucial. The success of treatment depends on its early initiation; however, there are still significant limitations in the therapies available to prevent renal injury secondary to rhabdomyolysis.

Open access peer-reviewed chapter