| Abstract [eng] |
Abstract Background: Artificial intelligence (AI), robotics, and advanced monitoring are transforming anesthesiology into a data-dependent specialty. Despite an increase in perioperative technical research, a significant discrepancy remains between algorithm development and clinical implementation. The objective of this thesis is to consolidate current scientific evidence regarding robotic anesthesia, precision anesthesia, remote monitoring, AI, and the metaverse to define an evidence-based roadmap for future perioperative care. Methods: A structured non-systematic literature review was conducted to enable a thematic synthesis of findings across diverse technological domains. The literature search was executed between August 2025 and April 2026 utilizing PubMed and Scopus as primary databases, augmented by supplementary searches and manual journal reviews. To ensure methodological transparency, the study selection process was documented using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. The final review comprised 47 included sources published between 2010 and 2026. Results: Preoperative predictive platforms leverage electronic health record datasets to forecast postoperative complications, achieving high predictive accuracy. Intraoperatively, pharmacological robots, specifically closed-loop delivery systems, utilize automated algorithms to maintain targeted physiological stability and anesthetic depth more consistently than manual titration. Mechanical robotic assistants, such as the Kepler Intubation System, enable remote-controlled endotracheal intubation, reducing human contact and enhancing operator precision. For regional anesthesia, AI- guided ultrasound tools identify anatomical structures with 99.7% accuracy, while robotic arms provide mechanical stability by eliminating human hand tremors. In the postoperative phase, continuous automated ward monitoring effectively minimizes "failure to rescue" events through the detection of pre-arrest physiological signals up to six hours prior to clinical deterioration. Conclusion: The integration of automated systems shifts the clinical focus from manual drug administration to real-time data interpretation and system oversight. Integrated digital workflows provide essential tools to enhance patient safety. Yet, widespread implementation is stalled. notable ethical, regulatory, and infrastructural barriers remain unresolved. The human clinician remains strictly necessary for managing complex clinical emergencies, ensuring that technology functions as a supportive instrument rather than an autonomous replacement. |
