Hemodynamic instability and uncontrolled hemorrhage remain leading causes of preventable morbidity and mortality in trauma and perioperative critical care. This review summarizes recent advances in machine learning-based approaches for early detection before overt decompensation and for supporting time-critical hemorrhage management in trauma patients. Recent studies have explored machine learning across multiple stages of trauma care, including early warning systems, outcome and mortality prediction, prediction of massive transfusion needs, risk stratification, and bleeding monitoring. Outcome prediction - particularly mortality and complications such as sepsis - remains one of the most extensively studied domains. More recent work has increasingly favored neural network-based architectures, including deep and hybrid models, reflecting their capacity to model complex, high-dimensional, and temporal physiologic data, while ensemble methods such as extreme gradient boosting remain widely used due to their robustness to missing data and class imbalance. Although many models outperform traditional clinical scores in retrospective analyses, performance frequently declines during external validation, and few systems have demonstrated clinical impact in prospective or workflow-integrated settings. Machine learning-based predictive analytics show promise for anticipating hemodynamic instability and guiding hemorrhage management before conventional vital-sign thresholds are crossed. However, clinical adoption remains constrained by data quality, generalizability, interpretability, and integration into time-critical workflows. Future progress will depend on incremental performance gains and physiology-informed model design, rigorous external validation, and careful positioning of machine learning tools as decision-support systems that augment - rather than replace - clinician judgment.
Trauma care demands rapid decision-making under uncertainty and time pressure, where maintaining situational awareness becomes challenging. Artificial intelligence offers potential to augment clinical judgment by processing complex physiological signals, predicting clinical trajectory, and enhancing shared mental models across care phases. This review examines recent developments in artificial intelligence-enabled triage and decision support across prehospital, emergency department, and mass-casualty settings. Machine learning models now predict lifesaving intervention needs from early prehospital data with performance comparable to expert judgment. Deep learning systems detect intracranial hemorrhage in real time, while dynamic risk scores continuously update mortality predictions during resuscitation. Artificial intelligence-enabled worklist triage accelerates critical imaging interpretation and procedural team activation. In mass-casualty scenarios, wearable sensors with automated triage algorithms maintain live patient classification, while drone-based computer vision enables contactless vital sign assessment. However, prospective evaluations reveal variable translation to measurable clinical benefit, requiring careful attention to usability, workflow integration, and ongoing calibration monitoring. Artificial intelligence has the potential to strengthen trauma triage by supporting cognitive work under pressure, but realizing this benefit requires rigorous evaluation extending beyond discrimination metrics to include calibration, clinical utility analysis, and human factors assessment. The most meaningful role for artificial intelligence is augmentation rather than automation, protecting clinicians' cognitive capacity for sound judgment.
Regional anaesthesia has well established benefits in paediatric perioperative care and acute pain interventions. This article explores current controversies and emerging evidence in paediatric regional anaesthesia. Despite rates of local anaesthetic systemic toxicity (LAST) remaining low, dosing of local anaesthetics in children remains controversial, particularly in neonates, in whom maximum safe doses can easily be exceeded. Local anaesthetic additives, such as dexamethasone, dexmedetomidine, and clonidine, are commonly used to prolong regional anaesthesia and have demonstrated good efficacy and safety, though best route of administration remains debated. Peripheral nerve catheters prolong analgesia and can be safely managed in ambulatory settings. Enhanced recovery after surgery (ERAS) is rapidly expanding in paediatric anaesthesia, and regional anaesthesia forms a key component. Fear of masking compartment syndrome is not supported by current literature and not a contraindication to regional anaesthesia. Studies exploring the utility of awake regional anaesthesia in children have successfully demonstrated the avoidance of general anaesthesia. Anaesthetic training increasingly demands proficiency in ultrasound-guided regional techniques. Regional anaesthesia is a safe and effective analgesic modality in children, including in trauma patients and as part of ERAS protocols, with good evidence for use of additives and low rates of complications, including LAST.
Climate change is already disrupting healthcare delivery with perioperative medicine, particularly pediatric anesthesia, being both highly exposed to climate-related shocks and a major contributor to healthcare-related greenhouse gas emissions (GHG). This review examines how mitigation and resilience strategies can be integrated into pediatric anesthetic practice. Sustainability measures in pediatric anesthesia are currently actionable and clinically beneficial. Reducing the use of volatile anesthetics through low-flow techniques, avoiding N2O or desflurane, and increasing the adoption of total intravenous anesthesia lead to substantial reductions in GHG and are associated with better clinical outcomes. EEG-guided anesthesia further reduces unnecessary exposure to anesthetics and improves recovery profiles. The use of reusable warming drapes or the implementation of 10R policies can markedly reduce our footprint without compromising the quality of care. Sustainable pediatric anesthesia is achievable today and aligns with improved clinical outcomes. Translating evidence into routine practice remains a challenge. Patient safety primacy or entrenched clinical habits continue to slow the adoption of sustainable practices, even when supported by robust data. Success will depend on reframing sustainability as a core component of quality and safety, embedding it within guidelines and audit structures, and supporting clinicians, thereby enabling durable behavior change.
This review examines recent advances in pediatric airway management, including emerging technologies, updated guidelines, and innovative strategies for improving safety across diverse clinical settings. We address the unique challenges faced when managing airways in neonates, infants, and children with complex conditions. The 2024 ESAIC-BJA neonatal and infant airway guidelines provide the first evidence-based recommendations emphasizing preoperative identification of the difficult airway, the use of neuromuscular blockade, videolaryngoscopy, and optimized preoxygenation to mitigate risk in this vulnerable population. Use of videolaryngoscopy continues to increase, with multicenter randomized controlled trials demonstrating 5-10% absolute improvements in first-attempt success rates and significant reduction in severe complications including esophageal intubation and hypoxemia, particularly when combined with supplemental oxygen during laryngoscopy. Registry and meta-analysis data now provide robust evidence that neuromuscular blocking agents improve intubation conditions and reduce complications. Artificial intelligence applications show promise for predicting difficult airways and optimizing endotracheal tube sizing. Front-of-neck access strategies have been refined, acknowledging the limitations of cricothyrotomy in young children. Extracorporeal membrane oxygenation has emerged as a rescue strategy in anticipated cannot-intubate-cannot-oxygenate scenarios. Global disparities in pediatric anesthesia safety persist, with collaborative educational initiatives addressing workforce challenges in low- and middle-income countries. The future of pediatric airway management lies in individualized, technology-enhanced approaches guided by evidence-based algorithms, multidisciplinary collaboration, comprehensive education, and simulation-based training, with a commitment to equitable care delivery worldwide.
Anesthesiology generates large volumes of heterogeneous perioperative data, including high-resolution physiological signals, clinical documentation, and device-generated information. Despite this richness, the clinical deployment of artificial intelligence systems remains limited. This review examines how limitations in data integration and systems interoperability constrain the translation of artificial intelligence into routine practice. Recent literature indicates that the principal barrier to artificial intelligence adoption in anesthesiology is not algorithmic performance but inadequate data integration. Data are distributed across anesthesia information management systems, electronic health records, and multiple medical devices, with variable data models, inconsistent semantic representations, and limited temporal synchronization. These systems were primarily designed for documentation, billing, and medicolegal purposes, rather than real-time analytics or secondary data use. In contrast, intensive care medicine has benefited from early investments in shared databases, which have facilitated reproducible artificial intelligence research and validation. Although emerging standards such as HL7 Fast Healthcare Interoperability Resources and common data models provide technical frameworks for interoperability, their implementation in anesthesiology remains partial. The effective deployment of artificial intelligence in anesthesiology depends on the development of interoperable, high-quality data infrastructures. Establishing standardized data models, semantic harmonization, temporal alignment, and robust data governance is a prerequisite for scalable, trustworthy artificial intelligence-enabled perioperative care.
This review summarizes the most recent advances in the field of chronic pain, highlighting how the discipline is shifting from heterogeneous approaches toward more standardized, mechanistic, and personalized frameworks, both in clinical care and research. Recent progress in phenotyping and person-centered analytical approaches (like based on the ergodicity concept) have been proposed to reduce the structural heterogeneity of studies. At higher levels, the implementation of the WHO International Classification of Disease, 11th Revision and the Enhancing and Facilitating the TRUST worthiness initiative are reshaping methodological thinking in pain research. These initiatives emphasize the importance of universal and robust frameworks in pain research. Clinically, recent data are reinforcing the role of active strategies, digital therapeutics, and novel mechanistic approaches, including new pharmacological targets and less (or non-) invasive neuromodulation approaches. Issues related to problematic opioid use remain central, underscoring the need for better-integrated multimodal models. Robust, reproducible, and less variable pain research and clinical practices are increasing. Future perspectives rely on large-scale phenotyping, longitudinal data, the integration of digital technologies, and precision-medicine approaches applied to chronic pain.
Enhanced recovery after surgery (ERAS) has evolved into a well established, evidence-based framework for perioperative care in numerous surgical disciplines. At the same time, advances in minimally invasive and catheter-based techniques have substantially expanded the number and complexity of procedures performed outside the operating room, leading to a rapid growth of nonoperating room anesthesia (NORA). Despite the clear overlap between ERAS principles and NORA patient needs, comprehensive recovery concepts for interventional procedures remain limited. Current evidence on ERAS-based approaches in NORA is sparse and heterogeneous, mainly originating from gastroenterology, cardiology, and interventional radiology. Existing studies suggest that selected enhanced recovery principles are feasible in interventional care and may improve patient comfort, recovery, safety, and procedural efficiency. However, implementation is often fragmented and lacks standardized, pathway-based peri-interventional management. Enhanced recovery principles hold substantial potential to improve peri-interventional care within the rapidly expanding NORA environment. The critical gap is not the absence of ERAS elements, but the absence of structured peri-interventional recovery governance comparable to established surgical ERAS pathways. Future progress will require holistic, multidisciplinary recovery frameworks, standardized concepts with procedure-specific adaptations, and clinical and economic evidence. Given its central role across the peri-interventional continuum, anesthesiology is well positioned to contribute to and potentially lead the development of structured enhanced recovery pathways beyond the operating room.
Nonoperating room anesthesia (NORA) and continuing education in this setting are both in a state of change. Procedures performed outside the operating room, especially those that require nuanced anesthetic care, are becoming increasingly prevalent. Numerous new innovations are transforming care across NORA settings, especially within gastroenterology, interventional and neurointerventional radiology, and electrophysiology. Concurrently, education for NORA providers is rapidly evolving, influenced by technological advancements and artificial intelligence. Traditional learning, a mode of education in which each learner receives essentially the same content, is now blending with adaptive learning, a field of precision education in which dynamic educational content is curated for individual learners, timed and paced to their preferences and progress, and coupled with artificial intelligence-generated feedback. The mainstays of traditional education for NORA providers remain relevant: content created by professional societies, podcasts, textbooks, and primary literature. Yet new, artificial intelligence-driven approaches for adaptive learning have entered the scene: clinical decision-making tools, literature review and knowledge mapping assistants, online platforms for precision education, virtual reality, and synthetic patients or avatars. This article explores the blending of traditional and adaptive educational modalities in NORA continuing education and discusses the implications and obstacles for the future.
Artificial intelligence is increasingly applied across the trauma care continuum, from prehospital triage to in-hospital decision-making. This review provides a timely synthesis of emerging applications, ethical challenges, and regulatory frameworks shaping the responsible integration of artificial intelligence into trauma systems. Recent studies highlight the potential of machine learning and deep learning models to improve trauma triage accuracy, imaging interpretation, and prediction of hemorrhage and transfusion needs. Despite promising accuracy, most systems remain in proof-of-concept phases with limited external validation. Ethical and governance challenges - particularly regarding data privacy, transparency, accountability, and automation bias - remain major barriers to clinical translation. The WHO guidance on artificial intelligence ethics and the European Union Artificial Intelligence Act establish core principles of safety, fairness, and human oversight, framing the foundation for trustworthy implementation. Artificial intelligence offers transformative opportunities for trauma care but requires rigorous validation, transparent governance, and structured clinician training to ensure safe, equitable, and ethically aligned deployment. Responsible, human-centered integration - anchored in oversight, algorithmic stewardship, and interdisciplinary collaboration - will be key to realizing full potential of artificial intelligence in trauma medicine.
To summarize recent evidence in pediatric total intravenous anesthesia (TIVA), highlighting advances in pharmacokinetics-pharmacodynamics, target-controlled infusion (TCI), electroencephalography (EEG)-guided titration, emerging agents, safety, and sustainability, and to provide clinicians with an updated, practical framework for pediatric TIVA practice. Recent evidence highlights major advances in pediatric TIVA, including clearer developmental pharmacokinetic-pharmacodynamic patterns, refined propofol-remifentanil dosing, and growing use of dexmedetomidine. Remimazolam shows promise but currently has limited pediatric evidence. Universal TCI models improve dosing accuracy across ages, while EEG-guided and combined pharmacokinetics-EEG strategies enhance safety in infants. TIVA reduces emergence delirium, postoperative nausea and vomiting, and perioperative respiratory adverse events; supports neurophysiologic monitoring; and yields substantially lower environmental greenhouse gas emissions than inhalation anesthesia. Pediatric TIVA is moving toward greater precision, safety, and sustainability. Moderate effect-site targets, opioid titration, and early down-titration remain central, particularly in neonates. Propofol infusion syndrome is exceedingly rare, and organ-protective effects of TIVA are reported in major surgery. Despite clinical and environmental advantages, adoption varies globally due to limited training, variable pump availability, and regulatory barriers. Expanding structured education and pediatric-specific TCI tools is essential for broader implementation.
Preoperative assessment is a central but increasingly complex component of pediatric anesthetic care. While perioperative safety in children has improved substantially, preventable complications remain prevalent. This review examines current concepts of pediatric preoperative evaluation, highlights emerging trends and controversies, and proposes the concept of anesthesia as a perioperative "navigator", whose guiding function begins with preoperative assessment. Contemporary practice of preoperative assessment increasingly includes structured risk stratification, procedure-specific planning, optimization of modifiable risk factors, and family centered communication. Advances highlight shorter fasting regimens, growing implementation of methods to optimize preexisting or chronic conditions such as pediatric patient blood management, refined strategies to manage upper respiratory tract infections and individualized approaches to manage preoperative anxiety. At the same time, digitalization, remote preassessment models, and delegation of assessment tasks are shaping clinical workflows. Shared decision-making and proactive communication have emerged as key determinants of cooperation for children and their families. Modern pediatric preoperative evaluation should be viewed less as a static medical checkpoint and more as starting for the dynamic process of perioperative "navigation". Integrating medical risk assessment with structured planning and effective communication remains essential to reduce complications and improve both safety and experience in pediatric anesthesia.
Chronic preoperative pain is increasingly recognized as a major determinant of perioperative complexity and postoperative outcomes. This review summarizes current evidence on the role of regional anaesthesia and analgesia (RAA) in adult surgical patients with pre-existing chronic pain, focusing on mechanistic rationale, short-term benefits, and potential long-term effects on persistent postoperative opioid use (PPOU) and chronic postsurgical pain (CPSP). Pre-existing chronic pain is common across surgical populations and is consistently associated with higher postoperative pain intensity, increased opioid requirements, delayed recovery, and poorer clinical outcomes. Psychological vulnerability, including anxiety, depression, and pain catastrophizing, further increases the risk of adverse pain trajectories and persistent opioid use. RAA provides effective opioid-sparing analgesia and may attenuate nociceptive transmission, central sensitization, and neuroinflammatory activation. Evidence strongly supports short-term analgesic benefits, whereas effects on PPOU and CPSP remain heterogeneous. RAA is a key component of individualized perioperative care in patients with chronic preoperative pain. Its greatest value lies in improving acute pain control, reducing opioid exposure, and supporting recovery. Long-term benefits are plausible but inconsistent, and are most likely when RAA is embedded within multimodal, phenotype-driven, and transitional pain care pathways.
With advances in technology, 30-50% of anesthesia cases have moved to nonoperating room anesthesia (NORA) sites. NORA challenges include patient and ergonomic complexity, which can complicate anesthesia care during routine and emergency situations. This review aimed to summarize current evidence on NORA crisis resource management (CRM). CRM in NORA is characterized by working with unfamiliar teams in locations not primarily designed for delivering anesthesia care. Equipment and personnel resources may be limited or different from what is available in the operating room. While few studies directly evaluate the effect of CRM in NORA settings, there is widespread consensus about the utility of crisis simulation training to improve team behaviors, role clarity, and timeliness of care. In addition, team building, process improvement, and the development of cognitive aids can be byproducts of well-executed CRM training programs. CRM can address several of the key challenges in NORA, including working in unfamiliar teams and in locations not primarily designed for anesthesia care. The paucity of studies may reflect the difficulty of implementing interprofessional CRM training in production-driven settings. Future research efforts should explore barriers to CRM training and successful implementation strategies in NORA.
Artificial intelligence in health is evolving rapidly, and there is a lot of hope that it may improve patient outcomes. The perioperative management of patients with major trauma is a challenge, as it requires rapid decision-making in complex and evolving clinical situations. Anesthesiologists are central to the early resuscitation, operative management, and postoperative supervision of these patients. Advances in artificial intelligence, together with the increasing availability of large trauma databases and real-time monitoring systems, have highlighted the potential role of artificial intelligence in trauma anesthesia. Artificial intelligence models may improve triage accuracy or assist clinicians in anticipating complications such as hemorrhagic shock, secondary brain injury, or prolonged stay in the hospital. Moreover, artificial intelligence-driven tools offer opportunities to individualize anesthetic and postoperative strategies by integrating patient characteristics, injury severity, or physiological responses. Yet, implementing these tools still faces important limitations, while it will require the training and their cultural adoption by a generation of physicians. This review aimed to report the current applications and future perspectives of artificial intelligence in the anesthetic management of severely injured patients. It also emphasizes its potential to enhance decision-making, personalize care, and ultimately improve patients' outcomes in trauma anesthesia.
Patients with chronic pain face elevated risks of inadequate postoperative analgesia, prolonged opioid use, and chronic postsurgical pain. Digital health technologies have expanded rapidly into perioperative care, yet their implications for chronic pain populations and for health equity remain insufficiently examined. This review synthesises recent evidence on digital health interventions for perioperative pain management while critically appraising how these technologies engage with, or exclude, people living with chronic pain. Five categories of digital intervention are identifiable, ranging from transitional pain services delivered via telehealth that actively target high-risk patients to general remote monitoring platforms that exclude chronic pain nuances and systematically marginalise digitally disadvantaged populations. Multicomponent platforms combining education, monitoring, and communication produce larger effect sizes than single-component interventions, yet most studies omit psychological dimensions and do not stratify for baseline chronic pain. Digital perioperative interventions risk reinforcing structural inequities unless guided by precision biopsychosocial models, multidimensional outcome measurement, equity-informed design, and hybrid care preserving in-person contact for those who need it most. Future trials must adopt core outcome sets measuring functional recovery, psychological distress, and chronic postsurgical pain transition.
To synthesize recent advances in intraoperative resuscitation for trauma surgery, including fluid composition, transfusion thresholds, and coagulopathy management, and to identify emerging directions that address persistent gaps in precision. Particular attention is given to prospective evidence, as forward-looking evaluations better capture real-world performance, workflow integration, and clinical impact, culminating in the integration of artificial intelligence and machine learning as the next stage of innovation. Major trials over the past 5 years have refined evidence for balanced transfusion ratios, restrictive transfusion thresholds, and the transition from crystalloid to hemostatic and physiology-guided strategies. However, outcome variability underscores the need for individualized, data-driven resuscitation. Early prospective machine learning evaluations - along with the US Food and Drug Administration-cleared point-of-care systems such as APPRAISE-HRI - demonstrate that multimodal physiologic modeling can predict hemorrhage and fluid responsiveness as accurately as expert clinicians, while offering real-time monitoring and guidance. The field is shifting from protocol-based to precision-guided trauma resuscitation. Future progress will rely on randomized-controlled-trial-validated, adaptive, human-in-the-loop artificial intelligence workflows that integrate continuous physiologic data with clinical judgment to enhance the timing, composition, and safety of intraoperative fluid and transfusion therapy.
Maternal morbidity and mortality remain largely preventable, yet current risk-assessment tools identify only a fraction of women who experience severe complications. This review synthesizes recent advances in artificial intelligence and machine learning for early prediction, decision support, and procedural guidance in obstetric anesthesia, with a focus on postpartum hemorrhage, hypertensive disease, sepsis, hemodynamic instability, neuraxial procedures, and peripartum pain. Recently, electronic health record (EHR)-integrated and imaging-based machine learning models have outperformed traditional risk scores for postpartum hemorrhage, placenta accreta spectrum, and pre-eclampsia, and are beginning to incorporate multiomics and genetic data. Obstetric-specific early warning systems and parsimonious machine learning models for maternal sepsis and epidural-related fever show promise but remain limited by sensitivity and external validation. Waveform analytics, noninvasive hemodynamic indices, and artificial intelligence-assisted ultrasound can anticipate hypotension and enhance neuraxial and regional block placement. Machine learning frameworks for postcesarean and chronic postpartum pain, together with virtual reality interventions, support more individualized analgesia. Artificial intelligence-enabled tools are poised to augment, rather than replace, clinician judgment in obstetric anesthesia. Real-world impact will depend on rigorous external validation, equitable implementation, interpretable model design, seamless EHR integration, and close collaboration between clinicians, data scientists, and vendors.
Advances in intravenous anaesthesia are driven by the need for agents with improved safety, enhanced hemodynamic stability, predictable recovery profiles, and overall patient safety. This review summarizes recent advances in intravenous anaesthesia with a particular focus on remimazolam, ciprofol, novel etomidate analogues, and neurosteroid anaesthetics and contemporary approaches to monitoring depth of anaesthesia. New agents aim to overcome limitations associated with traditional drugs such as propofol and etomidate. Remimazolam, an ultra-short-acting benzodiazepine provides effective hypnosis with reduced cardiovascular depression and the advantage of pharmacological reversibility. Ciprofol, a propofol analogue, demonstrates high potency, improved injection tolerability, and potentially more favorable hemodynamic profile. Etomidate analogues have been developed to retain anaesthetic efficacy, while minimizing adrenal suppression. Neurosteroid anaesthetics have reemerged as promising compounds due to rapid onset, stable cardiovascular effects, and favorable pharmacokinetics. Advances in target-controlled infusion and algorithm-driven automated drug delivery systems are enhancing precision and responsiveness of anaesthesia administration. Integrative technologies, including real-time monitoring and artificial intelligence support, are increasingly applied to optimize dosing and depth of anaesthesia. Over the recent half-decade, the field of intravenous anaesthesia reflects a shift toward personalized, computer-assisted delivery and novel agents with favorable pharmacokinetics. These trends point toward safer, more effective anaesthesia management while paving the way for continued innovation in clinical practice.
The purpose of this review is to present the most updated literature on care considerations for lactating patients in nonoperating room anesthesia (NORA) locations, including interventional radiology and gastroenterology. NORA procedures with anesthesia have been markedly increasing in volume with projections that it will comprise over 50% of anesthetics. Compared to surgery, NORA can be more complex for lactating patients, as it frequently involves additional procedural agents with varying degrees of breastfeeding compatibility because of medication transfer or physiologic challenges. While clinicians may be concerned about infant exposure to medications after patients receive anesthesia, it is important to support the breastfeeding dyad in the perioperative period and only interrupt breastfeeding if indicated. A 'sleep and keep' principle should be employed to minimize unnecessary interruption of breastfeeding. Lactating patients require perioperative support and collaboration between the anesthesiologist and proceduralist to maintain breastfeeding during an already stressful time. Evidence-based resources should be referenced to determine NORA-based medication compatibility and aid patient and physician decision making regarding lactation management perioperatively.