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AI and ML offer boundless possibilities on anesthesia practice

Recent advances in machine learning and artificial intelligence are likely to impact clinical anesthesia in the near future.

Technological advancement appears to have influenced all aspects of life in the universe, leading to remarkable changes in the society. Currently, the world is flooded with new technologies and scientific discoveries, and more are still emerging. This implies that these new technologies and scientific inventions will exert an immense impact on society. However, the wave of technological advancements seems to favor some fields with others experiencing the shock of technology impact. Interestingly, the field of healthcare and medicine is one of the beneficiaries of the newly emerging technologies; hence, medical practice has become relatively sophisticated owing to advanced delivery of care, treatment of chronic diseases, and identification of newly emerging diseases and health conditions. Healthcare systems and the scope of medical sciences of the twenty-first century are relatively different from the nineteenth-century versions because the impact of technological developments in the last decade seems to have transformed healthcare delivery systems with some of the newly invented technologies, producing extraordinary performances. Technological advancement in healthcare and medicine has not only led to the discovery of new drugs but has also improved safety and precision in clinical investigations. Moreover, it has led to the emergence of new medical procedures, which have, in turn, improved the quality of care offered to patients by healthcare professionals. For instance, the practice of anesthesia, which is seemingly built on technological advances, appears to have achieved a remarkable progress, especially with regard to the quality of care, owing to a number of technologies developed in the past decade.

It is worth noting that technological advancement in healthcare and medicine has not only led to the discovery of new drugs but has also improved safety and precision in clinical investigations.

Today, there is a deep chasm between artificial intelligence (AI)-based research articles and their translation into clinical anesthesia, which needs to be addressed. Machine learning (ML), the most widely applied arm of AI in medicine, confers the ability to analyze large volumes of data, find associations, and predict outcomes with ongoing learning by the computer. It involves algorithm creation, testing, and analyses with the ability to perform cognitive functions including association between variables, pattern recognition, and prediction of outcomes. AI-supported closed loops have been designed for pharmacological maintenance of anesthesia and hemodynamic management. Mechanical robots can perform dexterity and skill-based tasks, such as intubation and regional blocks with precision, whereas clinical-decision support systems in crisis situations may augment the role of the clinician. The possibilities are boundless, yet widespread adoption of AI is still far from the ground reality. Patient-related Big Data collection, validation, transfer, and testing are under ethical scrutiny.

Some of the principles and current advances in AI that are likely to impact clinical anesthesia in the near future are presented.

ML is now regarded as a mature technology and is employed in various industries across the world for quality control, error detection, and product recommendations. It involves collection and analysis of data by the computer, with a focus on model development and pattern detection to make intelligent predictions. Where humans can be overwhelmed by voluminous and complex data, machine learning models have the intrinsic capacity to provide valuable insights, based on extensive analysis and computation. Broadly classifying ML into supervised, unsupervised, and reinforcement learning is useful to understand applications. While pattern recognition is unsupervised, supervised applications include training and testing data sets. Reinforcement learning relies on continual feedback for algorithm modification. The ultimate goal of AI-based systems in anesthesia is to give the computer access to real-time patient data as well as information from medical publications; and from these, the system would integrate the various types of data and learn various relationships, producing actionable information. The main challenges are firstly, incompatibility between data from different electronic health record (EHR) systems and secondly, the cost of development.

Various ML models have been developed to answer single relevant clinical questions by using representative data, such as using perioperative data to predict postoperative mortality and post-induction hypotension by analyzing preoperative and induction data. The reliability of these models depends on the quality and methodology of the studies, on which they are based. Another issue is the difficulty clinicians face in understanding the mechanism by which prediction is done. This is especially seen in high-accuracy models, which are often complex and may not provide adequate insight into the why of a recommendation in a clinical scenario. It is important to combine accuracy with explanations in healthcare settings, and even more so in anesthesiology, where understanding of mechanisms is critical and consequences of incorrect prediction can be serious.

Robots in anesthesia. A robot is any mechanical system capable of interacting with the environment according to directed interventions. In medicine, the precise and reproducible interventions possible with robots make their role in complex surgery and anesthesia especially attractive. They free the anesthesiologist from repetitive technical tasks, allowing them to concentrate on overall assessment and decision-making. Robots are designed to support clinicians by automating tasks and offer pertinent recommendations based on the clinical scenario to aid decision-making.

Target controlled infusion (TCI) systems can be considered the first-generation open-loop pharmacological robots. Their software has built-in pharmacokinetic models of different drugs, based on which they deliver loading doses to achieve specific plasma drug levels rapidly and then maintain a steady state. They display estimated (not measured) plasma and effect-site concentrations, which may differ from the actual concentrations, especially in patients who display extreme anthropomorphic features or in patients with different racial characteristics. In closed-loop systems, a goal for a measured variable is set by the operator and drug delivery is adjusted to make the gap between the set goal and actual variable as small as possible. In the newer versions, refinements in algorithms, such as reinforcement learning, neural network, and fuzzy logic minimize fluctuations from the set target. The initial models were single-input-single-output (SISO) systems; recent advances have led to multiple-input-multiple-output (MIMO) robots, addressing hypnosis, analgesia, and muscle relaxation simultaneously. Anesthesia parameters, targeted by the SISO systems, initially included hypnosis (BIS-guided) or muscle relaxation (nerve-stimulator guided) as feedback for drug delivery. Subsequently, the Analgoscore was described for measuring pain during general anesthesia, calculated from the deviation of the patient’s target values for heart rate and mean arterial pressure. Hemodynamic parameters, used in SISO closed loop systems, include blood pressure, stroke volume, and stroke-volume variation.

BIS-guided autonomous systems have shown to be consistently superior to manually controlled propofol infusions in a number of trials. Reinforcement learning is now used to rapidly achieve steady state, based on a wide range of clinical parameters. A recent meta-analysis suggested that BIS-guided closed-loop systems provide better clinical performance than manual control of drug delivery for intravenous anesthesia. BIS, respiratory rate, and oximetry have been combined to develop a hybrid sedation system (HSS), and has been shown to outperform manual administration.

Recently, researchers have developed a closed-loop-assisted goal-directed fluid therapy system, which is guided by stroke volume and stroke-volume variation. Automated anesthesia delivery was combined with closed-loop fluid management in the ultimate MIMO system, where two independent CL systems were used for hypnosis, analgesia, and fluid management during major vascular surgery. Investigators are looking into the feasibility of combining fluid administration, along with appropriate vasopressor use, in a single automated system for guiding perioperative care.

Traditionally, logistic regression has been the basis of scoring indices widely applied for risk assessment in anesthesia. Today, ML is being studied for risk stratification, based on analysis of millions of perioperative data and intervention-based outcomes, extracted from EHR of multiple centers. This type of risk prediction is especially useful for counselling, optimization, and planning the anesthetic management of individual cases with rare co-morbidities. Deep neural network (DNN), a branch of AI, has been used by researchers for prediction of postsurgical mortality. Data related to nearly 60,000 patients was extracted from EHR, with 82 features included for each patient. The algorithm was trained on 80 percent of data and validated on the remaining 20 percent. The intrinsic ability to integrate pre-, intra-, and postoperative data seamlessly makes it superior to traditional risk assessment modules.

AI has been employed for airway assessment, and encouraging results reported include ability to correctly predict the Cormack-Lehane view on direct laryngoscopy with analysis of face and neck. Preoperative checklists have contributed to increased surgical safety. CDSS for selection of the most appropriate antibiotic and dosing schedule have been developed and widely tested in multicenter trials.

Applications of AI in the operation theater include monitoring and alarm fatigue, administration of anesthesia, hemodynamic management, and clinical decision support. Intraoperative cognitive robots can be integrated into alarm systems for simultaneous analysis of several parameters, thereby lowering the rate of false alarms. There is a growing interest in monitoring the target organ of anesthesia, the brain, via the electroencephalogram (EEG) as it may help in measuring the anesthetic effect. Currently, clinical trials are needed to validate any newly developed processed EEG monitor for each anesthetic drug due to specific differences in drug-induced EEG changes. Research in AI may eliminate the need to perform clinical trials on hypnosis-level monitors by the use of deep learning models.

Improvement of anesthesia provider’s productivity and patient outcome by using augmented intelligence, based on pooled patient data as well as incorporation of clinical guidelines, is the underlying goal for adoption of AI.

Only a minority of AI-based studies focus on integration of AI in daily clinical workflow in anesthesia, and just a handful of these have been shown to impact clinical care. Improvement of anesthesia provider’s productivity and patient outcome by using augmented intelligence, based on pooled patient data as well as incorporation of clinical guidelines, is the underlying goal for adoption of AI. While this may seem distant to some healthcare workers, there is a raging debate on the socio-economic impact of robots on the human workforce in every field.

Strong collaboration among physicians, scientists, manufacturers, regulators, and administra­tors has resulted in uniform electronic health records that can store and transmit data in advanced countries. However, there are several challenges in India, ranging from data availability to monitoring standards, adoption, and validation of fundamental technology instruments. Handwritten medical records continue to be the rule rather than an exception in advanced surgical setups. The timeline may be unpredictable, yet the medical profession cannot steer clear of this eventuality.

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