Hands-On to Hands-Free: The Evolution of Closed-Loop Drug Delivery System In Modern Anesthesia

Abstract

Traditionally, the administration of anesthesia has been managed by Anesthesiologists with constant adjustments and tweaks based on a combination of clinical and physiological parameters. Although this system has been effective for decades, it has its own disadvantages—human errors and the problem of determining the appropriate depth of anesthesia, which may result in the administration of more than the required amount. Progress in monitoring tools, pharmacokinetic stimulations, and algorithmic controls has spurred the creation of a closed-loop anesthesia delivery system (CLADS), which automates drug delivery through continuous feedback. To outline the historical progression of CLADS across key periods in contemporary anesthesia practice and assess their clinical impact, we performed a scoping systematic review following PRISMA-ScR standards. Searches of electronic databases identified studies on the design, testing, and use of CLADS. Data were abstracted and analyzed; CLADS development was grouped into eras defined by monitoring techniques, control mechanisms, and degrees of automation in the clinical sector. The initial manual phase depended on anesthesiologist-directed dosing based on vital signs and expert assessment. Semi-automated advancement brought target-controlled infusions and the Bispectrality index to guide adjustments. Fully closed-loop systems combined live anesthesia depth tracking with dynamic algorithms for automatic dosing of drugs. The new hands-free phase leverages multi-source data, machine learning, and AI for tailored, adaptive delivery requiring little human input. CLADS’s development from manual hands-on to completely hands-free automation represents a shift in the fundamentals to more exact, standardized, and personalized anesthesia practices. Understanding the highlights of each era underscores CLADS’s promise to enhance safety, reduce variability, and redefine automated perioperative practices.