Purpose of ReviewThis review explores the potential of artificial intelligence (AI) and machine learning (ML) in enhancing geriatric anesthesia education, highlighting the need for a paradigm shift in training and supporting anesthesiologists in the era of digital transformation.Recent FindingsAI and ML offer promising solutions for personalized learning experiences, real-time feedback, and data-driven decision-making in geriatric anesthesia education. Integrating AI simulations, adaptive learning modules, and AI-driven curricula can significantly enhance clinical skills and promote patient safety.SummaryThe integration of AI and ML in geriatric anesthesia education presents opportunities for adaptive and precision learning, enhanced proficiency in complex care coordination, and continuous professional development. Ethical considerations and the need for a collaborative environment are emphasized. Preparing anesthesiologists for AI integration requires focused education, comprehensive training, and a patient-centered approach. The synergy between AI capabilities and human expertise holds the potential to revolutionize geriatric anesthesia education and practice.

Abstract Purpose of Review Laparoscopic and robot–assisted procedures have become preferred over open abdominal surgery due to their numerous benefits, such as reduced postoperative pain, lower infection risk, and quicker recovery. However, these techniques pose unique challenges for intraoperative ventilation due to the need for CO2 insufflation to create a pneumoperitoneum and specific patient positioning, both of which adversely affect respiratory mechanics. This narrative review aims to summarize these challenges and describes current strategies to overcome them. Recent Findings Implementing lung–protective ventilation with low tidal volumes and personalized PEEP, along with measures to reduce mechanical power of ventilation, have been shown to reduce postoperative pulmonary complications (PPCs). Summary Effective and safe intraoperative ventilation during laparoscopic and robot–assisted surgery requires a nuanced understanding of respiratory mechanics and tailored ventilation strategies. Lung-protective ventilation is crucial for minimizing lung injury and optimizing patient outcomes.

Purpose of ReviewThe aim of this narrative review is to summarize critical considerations for perioperative airway management and mechanical ventilation in patients undergoing neurosurgical procedures.Recent FindingsGiven the significant influence that ventilation has on intracranial pressure (ICP) and cerebral blood flow, ventilator settings need to be carefully managed. For example, high positive end-expiratory pressure (PEEP) can increase ICP, while hyperventilation can reduce it. Finding the optimal balance is the key. While evidence supporting lung-protective ventilation in neurosurgical patients is limited, preliminary data suggest that its use could be beneficial, similar to general surgical patients. This typically involves using lower tidal volumes and maintaining optimal oxygenation to prevent ventilator-associated lung injury. Airway management in neurosurgical patients must consider the risk of increased ICP during intubation and the potential for airway complications. Techniques like rapid sequence induction and the use of neuromuscular blockers may be employed to minimize these risks. The primary goal of ventilation in neurosurgical patients is to maintain adequate oxygenation and carbon dioxide removal while minimizing harm to the lungs and brain. However, there may be exceptions where specific ventilatory adjustments are needed, such as in cases of compromised gas exchange or elevated ICP.SummaryPatients undergoing neurosurgical procedures often require invasive ventilation due to the complexities of the operation and the need to manage the airway. This creates unique challenges because ventilator settings must balance the need to protect both the lungs and the brain. Further research is needed to establish clear guidelines and optimize ventilatory care in this population.