There are different techniques for ML, such as, fuzzy logic, standard logic, neural networks, classical learning, deep learning, and Bayesian methods.

Artificial intelligence can provide guidance or suggestions from data it has analyzed to support clinical decision making such as alerting the clinician to possible drug-drug interactions for patient safety [52] . Anesthetists can use the abilities of AI-supported tools to provide safe care and improve patient outcomes from surgery [15] [31] [47] [66] [69] .

Topically, ChatGPT (Generative Pre-trained Transformers) is a natural language model that was launched in November 2022 and trained by OpenAI. ChatGPT has been tested in different areas including medicine, research, education and software engineering [20] . There is a concern of accuracy, as ChatGPT does not provide citations used to generate information for its answer, and when references are asked to be provided, ChatGPT in some cases provided unrelated or non-existent citations [65] [70] . The algorithms in ChatGPT use reinforcement type learning to improve their function by continuous feedback from complex real-world situations [71] . The drawback of this learning type is that it is high cost and time consuming. ChatGPT has the potential to revolutionize the healthcare industry by improving patient outcomes, reducing costs, and facilitating more efficient and accurate diagnosis and treatment [18] .

ChatGPT can use the natural language processing model to automate analysis of electronic medical record data. ChatGPT is currently being tested to extract information from free text such as EMR data or journal articles to build databases and learn [19] . It can then use inputted patient information to identify surgical candidates, assess for risk of adverse events, or facilitate billing [19] . Although ChatGPT provides promising outcomes in areas such as writing software codes and answering general questions, the gap between ChatGPT and applications in anesthesiology remains wide. Recently, ChatGPT was tested on different medical exams such as The United States Medical Licensing Exam (USMLE) and a Korean parasitology examination [72] [73] . ChatGPT performed poorly in the USMLE at a just pass level despite many correct answers to the questions currently published on the internet [70] [72] . Furthermore, ChatGPT’s performance on the parasitology examination was worse than that of medical students who sat the same exam in Korea [73] . ChatGPT has not been trained for medicine specifically, because of this, there are a number of limitations that still remain such as not being able to access medical research articles and an inability to provide a critique on a presented medical article [20] .

AlertWatch-OR by AlertWatch™ is a commercial clinical decision support (CDS) system, which is run by AI technology and designed specifically for use in anesthesiology [44] . It can provide case-specific advice after training using the available big data in combination with physiologic and laboratory data [58] [74] . This CDS system can detect adverse respiratory and haemodynamic events during anesthesiology and provide guidance for management of these intraoperative events to maintain stable anesthesiology and increase compliance to anesthesiology guidelines [74] [75] . Furthermore, the algorithm can be trained by supervised or non-supervised learning types and its ability is improved over time based on the availability of new data sets and user feedback [58] . The AIMS can be used to develop the CDS system to provide real-time advice to deliver safe patient care in anesthesiology as a requested corrective measure can be made immediately [31] [58] .

AlertWatch has a notable strength in its user-friendly visual display utilizing pictured organ systems, colour codes and text descriptions of alerts [76] .

SEDLINE® by Masimo (USA) monitors are commercial AI-supported tool in current clinical use [77] . They assess of depth of sedation using algorithms to process electroencephalogram waveforms to generate a Patient State Index between 0 (no activity) and 100 (alert) [78] . These devices can be unreliable as they heavily depend on the patient age, anesthetic agents used and interference from motion or electrocautery [47] .

The most important application of AI and machine learning in anesthetic practice is intraoperative management using closed-loop control algorithms. The typical feedback closed-loop control of intraoperative management is illustrated in Figure 1.

The closed-loop control algorithm of the AI robot collects and analyses a large amount of physiological data from many patients and draws patterns to actuate and maintain patient parameters at setpoints [51] . A closed-loop anesthesiology delivery system which intervenes only on the hypnotic component has successfully been used in the past for total intravenous anesthesiology induction and maintenance [79] . This is important for machine learning to enable prediction of the changes in control parameters (vital signs) of the patient during the operation [79] . During the anaesthetic, the algorithm is asked to detect if the patient parameters start to drift out the desired ranges, which are set by the anesthetist [79] . The algorithm will make the actuator adjust the drug injection rates to maintain a stable anaesthetic and alarm should the parameters move out of the expected control conditions [16] [79] . The difference between the conventional closed-loop control and AI-supported control is that the AI-supported tools take into account more control parameters and are able to learn from new training datasets to improve its performance with experience [51] .

Pharmacological robots are AI-supported systems which operated using a closed-loop control algorithm. These robots can inject the required doses of anaesthetic drugs into the patient to reach preset values to maintain a stable anesthetic [74] [79] . The possible advantage of pharmacological robots lies in rural and remote medicine to make anesthesiology accessible at anytime and anywhere without the need of the presence of a trained anesthetist [76] .

An AI-supported robot McSleepy was introduced by Hemmerling et al. in 2013 as a pharmacological robot which can autonomously control sedation, analgesia and neuromuscular blockade simultaneously during induction, maintenance and emergence [41] [74] . McSleepy is an automated closed-loop system delivering a hypnotic agent, analgesic agent and muscle relaxant, remifentanil and rocuronium targeting the parameters of BIS, analgoscore (nociception score based on blood pressure and heart rate) and muscle relaxation using train of four ratio [41] . Hemmerling et al. reported pilot studies of the efficacy of this system have shown good performance indices in a randomized control trial of 186 patients twenty patients undergoing elective thyroidectomies in Pisa, with their anesthesiology remotely controlled in Montreal, as well as its application in complex cardiac surgery [41] [80] [81] .

In an earlier iteration of McSleepy in 2008 as reviewed by Naaz et al., a closed-loop feedback control system maintaining anesthesiology depth using a hand-crafted rule and response algorithm was unsuccessful in management of complex cases [40] . To overcome this weakness, a neural network learning technique was applied [40] .

Recently, a closed-loop system for intravenous anesthesiology delivery has been developed named iControl-RP. This robot is able to make decisions and control the administration of the intravenous agents Propofol and Remifentanil [47] . iControl-RP takes into account neuromonitoring using NeuroSENSE (EEG based quantification of cortical activity including burst suppression ratio) monitoring as well as haemodynamic monitoring data into its decision making [42] . West et al. has found iControl-RP to perform at an acceptable level in clinical practice and is a feasible solution for propofol and remifentanil anesthesiology closed-loop control [42] .

Sedasys® by Ethicon (USA) was an AI-supported robot which can be managed by non-anaesthetic medical staff [82] [83] . However, this device has been withdrawn from the market in 2016, three years after its FDA approval in 2013 along with approval for use in Australia the European Union and Canada for gastrointestinal endoscopy [74] [84] . This robot performed a propofol sedation in healthy adults (patients of ASA 1 and 2). This robot records patient blood pressure, oxygen saturation, respiratory rate and automatically adjusted propofol infusion rate, oxygen flow and gave the operator cues to optimize patient care.

CLADS™ is a manual or automatic closed-loop system for propofol intravenous anesthesiology delivery. This system was developed at the Postgraduate Institute of Medical Education and Research, Chandigarh, India [85] . It uses BIS as the control variable and electroencephalographic indices as the input variable [85] . The CLADS™ system is able to use 5-secondly BIS feedback, taking into account BIS error, to adjust propofol infusion rates following a pharmacokinetic model to maintain preset BIS values [86] .

A clinical trial using CLADS™ was carried out in different groups of patients using the automatic mode of propofol infusion (non-cardiac surgery, cardiac surgery, postoperative sedation, and high-altitude surgery). The trial results showed CLADS™ was effective and efficient as compared to manual control and can be a useful tool to decrease cognitive load for anesthesiologists during anesthesiology [85] [86] .

RUGLOOP-IITM (Department of Anesthesiology, University Medical Center, Groningen, Netherlands) is a robust, patient-individualized closed-loop control system for propofol administration using BIS as control variable. This system uses Bayesian methodology for machine learning [87] . This system uses Bayesian variances to distinguish the specific patient model from the population model [87] . It was found that the RUGLOOP™ system could achieve induction within set time limits and less overshooting of BIS above pre-set values than human control [87] . The closed-loop control of anesthesiology maintenance had similar quality and movement scores to human controlled anesthesiology and was feasible in terms of hemodynamic and respiratory stability [87] .

The CONCERT-CL™ (VERYARK Technology Co., Ltd. (Guangxi, China)) is a closed-loop propofol and remifentanil infusion system to control intravenous anesthesiology [88] . This system, similar to others, is a target-controlled infusion (TCI) system and uses BIS as a control variable [88] . A clinical trial was performed in 2013 on 89 patients, who were between 18- and 65-years-old and have ASA classifications I or II and required general anesthesiology [88] . The CONCERT-CL was used to automatically regulate the TCI of propofol to maintain the BIS value in preset range [88] . The trial results show the CONCERT-CL system can regulate the TCI of propofol successfully as the BIS value is maintained in an adequate range [88] . While using this system reduces the workload of anesthetists’ significantly, it was only a valuable assistant which required the constant supervision of an anesthesiologist [88] .

Many other closed-loop control systems have been tested in the practice of anesthesiology, unfortunately their performances were not feasible and have not been further improved or tested to date [67] [89] [90] . The main challenges faced by these systems were patient safety and efficacy [83] .

Closed-loop anesthesiology delivery systems control the depth of anesthesiology with greater accuracy and using less sedative agents than human controlled anesthesiology as the automated systems change the medication rate more often than human anesthesiologists [29] [79] . This has a sedative agent sparing effect and may improve patient outcomes in the post operative period by reducing the incidence of delirium [42] .

Mechanical robots are AI-supported tools that also operate by a closed-loop control algorithm. These robots perform the manual tasks of an anesthesiologist automatically. The use of mechanical robots in anesthesiology can reduce some of the anesthesiologists’ repetitive actions. This gives the anesthetist more time to focus on patients and related perioperative issues, hence improving patient safety and service quality [51] [74] .

Endotracheal intubation and regional anesthesiology are the main proposed fields of application of mechanical robots.

Various types and techniques of machine learning have been tested for application of perioperative care risk prediction such as supervised learning, reinforcement learning, neural networks, deep learning and fuzzy logic. Preoperatively, risk assessment is an invaluable tool for both elective and emergency surgical cases where prediction of mortality and/or morbidity has the potential to change perioperative management [94] . An intraoperative example is neural networks that can be applied for prediction of the hypnotic effect of an induction bolus dose of propofol with greater accuracy than the average estimate of anesthesiologists in practice [95] .

Ultrasound guided technique is commonly used in mechanical robots as described above such as the Magellan system used to perform regional anesthesiology [96] . The most common machine learning technique employed to achieve ultrasound image classification is neural networks and deep learning [47] . Smistad and Løvstakken used 15 ultrasound images of patient’s groins to train a convolutional neural network in identifying femoral artery and vein, distinguishing it from other femoral anatomical structures such as muscle, bone or acoustic shadow with an accuracy of 94.5% ± 2.9% on average [97] .

There are currently no commercial AI products available for widespread use in the area of pain management in the perioperative setting. Attempted AI application in pain management has a wide range from the processing of brain imaging for identifying pain to prediction of opioid dose response using biomarkers [47] .

Images were collected from human volunteers who had functional magnetic resonance imaging while exposed to painful and non-painful thermal stimulation and used machine learning to analyse their differences and similarities. They found that machine learning analysis the brain images as a whole more accurately identifies pain than the analysis of individual brain regions associated with nociception [47] .

PainChek™ is an AI tool used to identify pain in patients with dementia and is designed for use in Geriatric medicine and therefore is outside the scope of this review on AI-supported systems in anesthesiology [98] .

Gram et al. analysed electroencephalopathy signals from 81 patients using machine learning techniques and predicted with 65% accuracy which patients would respond to post-operative opioid therapy [99] .

Algorithms have been developed to analyze complex EEG waveforms using machine learning techniques to create an index directly related to measurement of depth of anesthesiology [47] . The strength of using sophisticated neural networks and deep learning techniques is the ability of the algorithm to engage in unsupervised learning to identify patterns that will best predict target values such as awareness based on EEG as well as haemodynamic data [100] . The AI algorithm is able to predict this more reliably than a human expert [47] .

Combes et al. trained a neural network using data from an EMR database containing information of staffing, operating room flow and time in post anesthesiology care unit to predict duration of one episode of surgical care [101] . The prediction is based on operating team, type of operation and relevant patient medical history. It is notable that the accuracy of their models never exceeded 60% [101] .

Another example of combined fuzzy logic and neural network techniques to optimize bed use is by Devi et al. who modelled type of surgery, surgical team experience, type of anesthesiology, experience of the anaesthetic team and relevant patient factors in ophthalmologic surgery [102] . They achieved accuracy between 81% - 86% in prediction of duration of surgical care episode depending on case type [102] .

In these models, the potential of AI technology is not to replace the anesthesiologist, but to augment and improve the workflow and decision making of the surgical and anesthesiology teams [47] .

Summarized below (Table 1) are the AI-supported tools in anesthesiology at late stages of development reviewed in this article.

AI machine performs prediction or answers a question by using medical training data, which must be reliable and connected to a specific case in a strict format. Unfortunately, a standard data format for ML is not available at present and there exists much incompatibility between data formats from different EMR systems [3] [46] [51] . Furthermore, the quantity and quality of the training data is very important as if small datasets or irrelevant data is used for the algorithm training process, then the output will be similarly unreliable when used to aid decision making [105] .

Current AI developments are based on differing data formats that is not conducive to international adoption. To accelerate and encourage the development and subsequent application of AI technology in anesthesiology, the author believes a common data format between international EMRs is a vital first step to the further development of AI systems for use in anesthesiology. The aforementioned AIMS is a good example of a widely adopted software enabling perioperative patient data collection [17] .

In order for the AI-supported systems to work effectively, they should process big data and give accurate outputs in real time. Currently, acquiring and processing high velocity and large data volumes in an operating room setting is a challenge due to the multiple and complex algorithms used in AI-supported robots for use in anesthesiology [58] . The author believes due to the limited resources in healthcare, the plausibility of upgrading expensive computer systems to support each new generation of algorithms is unrealistic. Therefore, the algorithms should be designed to be simplified and efficient.

AI robots answer questions based on the information from many factors, but are unable to take into account the variability of significance of each factor for individual patients as there may be wide variation. For example, ML works based on general models such as the pharmacokinetic model, which adequately calculates plasma concentration of drugs, but is unable to predict the effect of such plasma concentration on patient sedation [88] . Furthermore, the monitoring on which measurement of adequate sedation and unawareness is based, predominantly EEG, remains notoriously unreliable [106] . Due to this inadequacy, AI algorithms are still not competent enough in practice to surpass human performance [40] .

The beginnings of development to overcome the wide variability between individual patients can be seen in the novel robots using Bayesian method for machine learning. The continuous updating of data from experience theoretically enables the robots to adapt its decision making from real time data updates during anesthesiology. The author suggests that improvement of user-friendly graphic interfaces and algorithms to enable increased individual patient data inputs is important for the personalization of AI anesthesiology and hence improve patient outcomes.

Anesthetists make complex considerations for each case involving collecting patient information, review of medical data, designing anaesthetic plans, and performing individualized clinical procedures. The anesthetist works in a multidisciplinary team including the surgeon, pharmacists, medical specialties, pre-habilitation allied health staff etc. to negotiate the surgical plan and perform the optimal anesthesiology. Human intelligence is used to deal with many complex medical and interpersonal situations simultaneously, which mathematical algorithms are unable to replicate [54] . Further to this, as summarized in Table 1, the current pharmacological robots use electroencephalographic indices as a main or only control parameter as a measure of sedation. However, these values are notoriously unreliable and are affected by much interference [21] . Current developed ML algorithms are neither sophisticated enough nor have adequate ability to integrate all aspects of an anesthesiologist’s work; thus, it is impossible for AI technology to replace an anesthesiologist in the near future [40] .

Human intelligence can be used to deal with abnormal cases; thus skill, experience and knowledge of an anesthetist are combined to make life saving decisions. The AI machine actions or gives predictions following mathematical rules, the predicting algorithms used in AI systems do not support human intervention in a clinical setting. It is difficult for the AI user to know the appropriate actions to take when the robot gives a warning or makes an unexpected decision during anesthesiology [54] . Because of this potential need for rescuing interventions, it is unlikely that anesthetic delivery systems would ever function fully autonomously without clinician supervision [55] .

For anesthesiologists to confidently use AI robots, clear visualization features rich in information and explanation needs to be an integral part of the machines. The author suggests that these graphic interfaces should be designed by anesthesiologists to appropriately rationalize information displayed. Computational or engineering related errors which are out of the scope of practicing anesthesiologists should be minimized to avoid unnecessary apprehension.

The author believes, to improve safety and reliability, AI needs time to prove its ability and improvements implemented before it is applied in daily practice safely as patients cannot be treated as test subjects for novel technology with little evidence base. This is a lesson learned from the development of McSleepy, which in its early version used a hand-crafted rule and response algorithm and did not work well to handle complex cases. Later improvements were made by applying neural network machine learning [40] .

The deskilling phenomenon could be seen when the regional block needle tracking system was utilized [21] . This potential dependence during the training process on AI technology may decrease overall competence among trainees despite reduction in variation [21] . Skills are maintained and improved through experience. The fear then becomes if anesthesiologists come to rely heavily on automated systems, it would lead to the decrease in skills such as intubation or titration of sedatives [33] . Anesthesiologists need to maintain their specialized skill through practice in the cases of technological failures or emergency situations. The author firmly believes the development of AI systems for use in anesthesiology therefore should be designed with the aim of assisting, not replacing anaesthesists.

Further to clinical skills, anaesthesists have a very involved role in teaching trainees through sharing of hands-on experience and valuable insights during cases which cannot be replaced by AI technology such as natural language models [20] .

As we are all familiar with, sophisticated modern computers are still subject to error or failures without warning which can result from software defects, data artifact, hardware failures or logic errors [58] . Potentially significant morbidity or mortality can occur to a patient if such errors transpire in anesthesiology to trigger inappropriate decision making or administering incorrect drugs [58] .

While error in technology remains an inevitable occurrence, there are some improvements which can decrease the rate of error and failure [107] . For this, we can learn and consider strategies from the aviation industry, one with established low failure and error rates despite the complex engineering of AI-supported aircrafts [107] . The first thing to consider is the balance between quality of build and cost-effectiveness of the systems [107] . While the author is aware a compromise will always need to be made, rationalization of the areas of cost-cutting is important as to not compromise patient safety. There should be strict and clear maintenance services if the widespread use of AI-supported systems were to become widely used with clear documentation in logbooks. The machines should be made from high quality components by specialized manufacturers. There should also be an automatic and neutral recording system of errors which cannot be tampered with similar to the black boxes (flight recorders) on aircrafts.

Due to the complexities of processing by AI algorithms applying deep learning, how the decisions are made can be difficult to understand, even by the engineers that understand the used mathematical algorithms [46] [47] [54] . Furthermore, commercialized AI systems often have proprietary algorithms which usually remain confidential, much like the method of BIS calculation [108] . This opacity means AI systems operate by “black box” functioning model which understandably creates apprehension among anesthesiologists, discouraging them to rely on AI systems in practice.

Explainability of the decisions made is vital to increase the transparency of AI systems and encourage its trust and integration in anaesthetic practice [109] . This is particularly important in cases where there is a differing opinion between anesthesiologists and AI systems as knowing how the respective decisions are made will enable comparison, rationalization and thus enactment of the optimal decision.

The author proposes, to improve the transparency, the algorithms should include algorithm explanation windows for how decisions are reached as well as for reasoning of error messages that are written in plain language. This would increase the trust anesthesiologists have for AI technology in anesthesiology and therefore increase its’ implementation and acceptance in daily practice.

There exist many obstacles in the strict guidelines and regulations for the implementation of AI technology in clinical practice, and for good reason due to the high stakes nature of anesthesiology [56] . However, this causes a delay in the development and adoption of AI technology in hospitals around the world as only limited AI technologies have been approved for use. For example, only static or locked algorithms that return expected and predictable outcomes have been approved by the FDA [47] .

This is well illustrated in the case of the pharmacological robot Sedasys™. Its failure on the market was in part due to the highly restrictive and conservative function that was programmed by the developers in order to satisfy the criteria for FDA approval [84] . This particular robot was restricted to only being able to titrate propofol infusions down, and required an anesthesiologist to manually increase the dose if the patient’s sedation lightened [84] . Therefore, not only was this device have limited usefulness in clinical practice, it may also have decreased the interest of using AI systems in anesthesiology.

Another challenge exists in patient privacy and confidentiality when accessing patient big data for inputs in AI systems [31] . For this reason, there needs to be regulatory changes to enable assumed consent and an opt-out system to access patient data [94] .

Health regulators need to make strict laws to regulate AI application to prevent harm to patients. It is difficult for regulators to approve complex, “black box” AI-supported robots, as they need a long time to prove their safety and reliability, therefore delaying their development and implementation. The author suggests a potential solution lays in a modular design strategy where simple AI algorithms should be designed and tested first, then slow integration of more algorithm units to build a more complex system.

At present, few anesthesiologists are familiar with the way AI robots work or are involved in the development of these machines [40] . As a consequence of this opacity, there will be less confidence and acceptance in using AI robots in practice hence slowing the development and implementation of AI robots in the routine practice of anesthesiology [54] .

Furthermore, given the current general lack of knowledge and experience of AI in anesthesiology, the anesthesiologist will be met with difficult situations when AI robots make unexpected decisions, thus creating a barrier to using these machines in regular practice.

The author thinks the key to the success of AI applications in anesthesiology is to begin at the beginning. Anesthetists should be heavily involved in the design of AI-supported systems for use in anesthesiology as this enables the rationalization of important features in clinical use. Furthermore, there exists an ongoing need for further clinical studies evaluating the relevance of AI-supported systems and their effectiveness in real-world applications through rigorous outcome assessments.

All anesthesiology personnel should be trained to understand AI technology as without knowledge and experience, and the user will likely achieve poor outcomes using AI-supported systems and feel mistrust for these systems [40] . The author suggests training should include medical staff at all levels, from medical students to senior clinicians, if the introduction of these systems in anesthesiology is to succeed. To achieve this, AI training courses for medical students, junior doctors and anesthetists should be provided frequently to normalize the presence of AI in medicine.

Earlier in this article, there has been a discussion of multiple areas of anesthesiology where AI has potential applications, including pain medicine, event prediction and operating room logistics. However, most robots at later developmental stages are focused on pharmacological and mechanical robots with a very direct potential impact on patient morbidity and mortality. To improve the reception and trust of AI systems in anesthesiology, development of AI could begin with diagnostic and logistical supports first, which can improve patient outcomes without direct potential for harm.