The field of anaesthesiology and perioperative medicine has explored advancements in science and technology, ensuring precision and personalized anesthesia plans. The surge in the usage of chat-generative pretrained transformer (Chat GPT) in medicine has evoked interest among anesthesiologists to assess its performance in the operating room. However, there is concern about accuracy, patient privacy and ethics. Our objective in this study assess whether Chat GPT can provide assistance in clinical decisions and compare them with those of resident anesthesiologists.

In this cross-sectional study conducted at a teaching hospital, a set of 30 hypothetical clinical scenarios in the operating room were presented to resident anesthesiologists and Chat-GPT 4. The first five scenarios out of 30 were typed with three additional prompts in the same chat to determine if there was any detailing of answers. The responses were labeled and assessed by three reviewers not involved in the study.

The interclass coefficient (ICC) values show variation in the level of agreement between Chat GPT and anesthesiologists. For instance, the ICC of 0.41 between A1 and Chat GPT indicates a moderate level of agreement, whereas the ICC of 0.06 between A2 and Chat GPT suggests a comparatively weaker level of agreement.

In this study, it was found that there were variations in the level of agreement between Chat GPT and resident anesthesiologists' response in terms of accuracy and comprehensiveness of responses in solving intraoperative scenarios. The use of prompts improved the agreement of Chat GPT with anesthesiologists.

In medicine, Artificial intelligence has begun to be utilized in nearly every domain, from medical devices to the interpretation of imaging studies. There is still a need for more experience and more studies related to the comprehensive use of AI in medicine. The aim of the present study is to evaluate the ability of AI to make decisions regarding anesthesia methods and to compare the most popular AI programs from this perspective.

The study included orthopedic patients over 18 years of age scheduled for limb surgery within a 1-month period. Patients classified as ASA I-III who were evaluated in the anesthesia clinic during the preoperative period were included in the study. The anesthesia method preferred by the anesthesiologist during the operation and the patient's demographic data, comorbidities, medications, and surgical history were recorded. The obtained patient data were discussed as if presenting a patient scenario using the free versions of the ChatGPT, Copilot, and Gemini applications by a different anesthesiologist who did not perform the operation.

Over the course of 1 month, a total of 72 patients were enrolled in the study. It was observed that both the anesthesia specialists and the Gemini application chose spinal anesthesia for the same patient in 68.5% of cases. This rate was higher compared to the other AI applications. For patients taking medication, it was observed that the Gemini application presented choices that were highly compatible (85.7%) with the anesthesiologists' preferences.

AI cannot fully master the guidelines and exceptional and specific cases that arrive in the course of medical treatment. Thus, we believe that AI can serve as a valuable assistant rather than replacing doctors.

Artificial intelligence (AI) technology is vital for practitioners to incorporate AI and robotics in day-to-day regional anesthesia practice. Recent literature is encouraging on its applications in regional anesthesia, but the data are limited. AI can help us identify and guide the needle tip precisely to the location. This may help us reduce the time, improve precision, and reduce the associated side effects of improper distribution of drugs. In this article, we discuss the potential roles of AI and robotics in regional anesthesia.

This review examines recent research on artificial intelligence focusing on machine learning (ML) models for predicting postoperative pain outcomes. We also identify technical, ethical, and practical hurdles that demand continued investigation and research.

Current ML models leverage diverse datasets, algorithmic techniques, and validation methods to identify predictive biomarkers, risk factors, and phenotypic signatures associated with increased acute and chronic postoperative pain and persistent opioid use. ML models demonstrate satisfactory performance to predict pain outcomes and their prognostic trajectories, identify modifiable risk factors and at-risk patients who benefit from targeted pain management strategies, and show promise in pain prevention applications. However, further evidence is needed to evaluate the reliability, generalizability, effectiveness, and safety of ML-driven approaches before their integration into perioperative pain management practices.

Artificial intelligence (AI) has the potential to enhance perioperative pain management by providing more accurate predictive models and personalized interventions. By leveraging ML algorithms, clinicians can better identify at-risk patients and tailor treatment strategies accordingly. However, successful implementation needs to address challenges in data quality, algorithmic complexity, and ethical and practical considerations. Future research should focus on validating AI-driven interventions in clinical practice and fostering interdisciplinary collaboration to advance perioperative care.

Anesthesiology is considered a complex medical specialty. Its history has been marked by radical advances and profound transformations, owing to technical and pharmacological developments and innovations in the field, enabling us over the years to improve patient outcomes and perform longer, more complex surgical procedures on more fragile patients. However, anesthesiology has never been safe and free of challenges. Despite the advances made, it still faces risks associated with the practice of anesthesia, for both patients and healthcare professionals, and with some of the specific challenges encountered in low and middle-income countries. In this context, certain actions and initiatives must be carried out collaboratively. In addition, recent technologies and innovations such as simulation, genomics, artificial intelligence, and robotics hold promise for further improving patient safety in anesthesiology and overcoming existing challenges, making it possible to offer safer, more effective, and personalized anesthesia. However, this requires rigorous monitoring of ethical aspects and the reliability of the studies to reap the full benefits of the new technology. This literature review presents the evolution of anesthesiology over time, its current challenges, and its promising future. It underlines the importance of the new technologies and the need to pursue efforts and strengthen research in anesthesiology to overcome the persistent challenges and benefit from the advantages of the latest technology to guarantee safe, high-quality anesthesia with universal access.

The potential applications of Artificial Intelligence (AI) in anaesthesia are expansive.~However, like any technological advancement, the integration of AI in anaesthetic practice comes with both benefits and potential risks. This article seeks to set out some of the advantages and disadvantages of the use of AI technologies within the field of anaesthesia. Benefits of the application of AI in anaesthesia include an improvement in perioperative risk stratification, personalisation of anaesthetic plans, improvement in efficiency and ultimately reduce healthcare costs. However, reliance on technology may reduce clinical acumen but furthermore there are issues surrounding data quality, privacy as well as legal and ethical concerns, which require further evaluation. Whilst AI within anaesthetic practice holds immense promise, there are substantial challenges which require careful consideration and ongoing evaluation. A collaborative approach will be required from healthcare staff, developers and regulators to promote the safe, responsible, and effective application of AI in anaesthesia practice.

➤ The COVID-19 pandemic created a persistent surgical backlog in elective orthopedic surgeries. ➤ Artificial intelligence (AI) uses computer algorithms to solve problems and has potential as a powerful tool in health care. ➤ AI can help improve current and future orthopedic backlogs through enhancing surgical schedules, optimizing preoperative planning, and predicting postsurgical outcomes. ➤ AI may help manage existing waitlists and increase efficiency in orthopedic workflows.

In particular, the application of regional anaesthesia techniques in existing medicine can be characterized as experiencing regular changes in recent decades. It is useful for obtaining accurate and efficient pain management solutions, from the basic spinal and epidural blocks to the novel ultrasound nerve blocks and constant catheter procedures. These advancements do enhance not only the value of the perioperative period but also the patient's rated optimization as enhancing satisfaction, better precision, and the safety of nerve block placement. The use of ultrasound technology makes it even easier to determine the proper positioning of the needle and to monitor nerve block placement. Moreover, the duration and efficiency of regional anaesthesia are being enhanced by state-of-the-art approaches, which come in the form of liposomal bupivacaine, and better recovery plans and protocols, which shorten recovery time and decrease the number of hospital days. As these methods develop further, more improvements in the safety, efficacy, and applicability of regional anaesthesia in contemporary medicine are anticipated through continued research and innovation.

Anesthetic drugs play a vital role during surgery, however, due to individual differences and complex physiological mechanisms, the prediction of anesthetic drug dosage has always been a challenging problem. In this study, we propose a model for predicting the dosage of anesthetic drugs based on deep learning to help anesthesiologists better control their dosage during surgical procedures.

We design a model based on the artificial neural network to predict the dosage of preoperative anesthetic, and use the SELU activation function and the loss function for weighted regularization to solve the problem of unbalanced sample. Moreover, we design a CNN-based model for the prior extraction of intraoperative features by using a 7 × 1 convolution kernel to enhance the receptive field, and combine maximum pooling and average pooling to extract key features while eliminating noise. A predictive model based on the LSTM network is designed to predict the intraoperative dosage of the anesthetic, and the bidirectional propagation-based LSTM network is used to improve the ability to learn the trend of changes in the physiological states of the patient during surgery. An attention module is added before the connection layer to appropriately attend to areas containing prominent features.

The results of experiments showed that the proposed method reduced values of the MAPE to 15.83% and 12.25% compared with the traditional method in predictions of the preoperative and intraoperative doses of the anesthetic, respectively, and increased the values of R 2 to 0.887 and 0.915, respectively.

The intelligent anesthesia prediction algorithm designed in this study can effectively predict the dosage of anesthetic drugs needed by patients, assist clinical judgment of anesthetic drug dose, and assist the anesthesiologists to ensure the smooth progress of the operation.

Success in integrating artificial intelligence (AI) in anesthesia depends on collaboration with anesthesiologists, respecting their expertise, and understanding their opinions. The aim of this study was to illustrate the confidence in AI integration in perioperative anesthetic care among Jordanian anesthesiologists and anesthesia residents working at tertiary teaching hospitals. This cross-sectional study was conducted via self-administered online questionnaire and includes 118 responses from 44 anesthesiologists and 74 anesthesia residents. We used a five-point Likert scale to investigate the confidence in AI's role in different aspects of the perioperative period. A significant difference was found between anesthesiologists and anesthesia residents in confidence in the role of AI in operating room logistics and management, with an average score of 3.6 ± 1.3 among residents compared to 2.9 ± 1.4 among specialists (p = 0.012). The role of AI in event prediction under anesthesia scored 3.5 ± 1.4 among residents compared to 2.9 ± 1.4 among specialists (p = 0.032) and the role of AI in decision-making in anesthetic complications 3.3 ± 1.4 among residents and 2.8 ± 1.4 among specialists (p = 0.034). Also, 65 (55.1%) were concerned that the integration of AI will lead to less human-human interaction, while 81 (68.6%) believed that AI-based technology will lead to more adherence to guidelines. In conclusion, AI has the potential to be a revolutionary tool in anesthesia, and hesitancy towards increased dependency on this technology is decreasing with newer generations of practitioners.

This review article examines the utility of artificial intelligence (AI) in anesthesia, with a focus on recent developments and future directions in the field. A total of 19,300 articles were available on the given topic after searching in the above mentioned databases, and after choosing the custom range of years from 2015 to 2023 as an inclusion component, only 12,100 remained. 5,720 articles remained after eliminating non-full text. Eighteen papers were identified to meet the inclusion criteria for the review after applying the inclusion and exclusion criteria. The applications of AI in anesthesia after studying the articles were in favor of the use of AI as it enhanced or equaled human judgment in drug dose decision and reduced mortality by early detection. Two studies tried to formulate prediction models, current techniques, and limitations of AI; ten studies are mainly focused on pain and complications such as hypotension, with a P value of <0.05; three studies tried to formulate patient outcomes with the help of AI; and three studies are mainly focusing on how drug dose delivery is calculated (median: 1.1% ± 0.5) safely and given to the patients with applications of AI. In conclusion, the use of AI in anesthesia has the potential to revolutionize the field and improve patient outcomes. AI algorithms can accurately predict patient outcomes and anesthesia dosing, as well as monitor patients during surgery in real time. These technologies can help anesthesiologists make more informed decisions, increase efficiency, and reduce costs. However, the implementation of AI in anesthesia also presents challenges, such as the need to address issues of bias and privacy. As the field continues to evolve, it will be important to carefully consider the ethical implications of AI in anesthesia and ensure that these technologies are used in a responsible and transparent manner.

Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context.

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted.

A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods.

AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.

Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.

The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.

The last 2 decades have brought important developments in anesthetic technology, including robotic anesthesia. Anesthesiologists titrate the administration of pharmacological agents to the patients' physiology and the needs of surgery, using a variety of sophisticated equipment (we use the term "pilots of the human biosphere"). In anesthesia, increased safety seems coupled with increased technology and innovation. This article gives an overview of the technological developments over the past decades, both in terms of pharmacological and mechanical robots, which have laid the groundwork for robotic anesthesia: target-controlled drug infusion systems, closed-loop administration of anesthesia and sedation, mechanical robots for intubation, and the latest development in the world of communication with the arrival of artificial intelligence (AI)-derived chatbots are presented.

This review explores the intersection of artificial intelligence (AI) and anesthesia, examining its transformative impact on patient care across various phases. Beginning with a historical overview of anesthesia, we highlight the critical role of technological advancements in ensuring optimal patient outcomes. The emergence of AI in healthcare sets the stage for a comprehensive analysis of its applications in anesthesia. In the preoperative phase, AI facilitates personalized risk assessments and decision support, optimizing anesthesia planning and drug dosage predictions. Moving to the intraoperative phase, we delve into AI's role in monitoring and control through sophisticated anesthesia monitoring and closed-loop systems. Additionally, we discuss the integration of robotics and AI-guided procedures, revolutionizing surgical assistance. Transitioning to the postoperative phase, we explore AI-driven postoperative monitoring, predictive analysis for complications, and the integration of AI into rehabilitation programs and long-term follow-up. These new applications redefine patient recovery, emphasizing personalized care and proactive interventions. However, the integration of AI in anesthesia poses challenges and ethical considerations. Data security, interpretability, and bias in AI algorithms demand scrutiny. Moreover, the evolving patient-doctor relationship in an AI-driven care landscape requires a delicate balance between efficiency and human touch. Looking forward, we discuss the future directions of AI in anesthesia, anticipating advances in technology and AI algorithms. The integration of AI into routine clinical practice and its potential impact on anesthesia education and training are explored, emphasizing the need for collaboration, education, and ethical guidelines. This review provides a comprehensive overview of AI applications in anesthesia, offering insights into the present landscape, challenges, and future directions. The synthesis of historical perspectives, current applications, and future possibilities underscores the transformative potential of AI in revolutionizing patient care within the dynamic field of anesthesia.

Striking innovations and advancements have been achieved with the use of artificial intelligence and healthcare information technology being integrated into clinical real-world data. The current scoping review aimed to provide an overview of the current status of artificial intelligence-/information technology-based clinical decision support tools in China.

PubMed/MEDLINE, Embase, China National Knowledge Internet, and Wanfang data were searched for both English and Chinese literature. The gray literature search was conducted for commercially available tools. Original studies that focused on clinical decision support tools driven by artificial intelligence or information technology in China and were published between 2010 and February 2022 were included. Information extracted from each article was further synthesized by themes based on three types of clinical decision-making.

A total of 37 peer-reviewed publications and 13 commercially available tools were included in the final analysis. Among them, 32.0% were developed for disease diagnosis, 54.0% for risk prediction and classification, and 14.0% for disease management. Chronic diseases were the most popular therapeutic areas of exploration, with particular emphasis on cardiovascular and cerebrovascular diseases. Single-center electronic medical records were the mainstream data sources leveraged to inform clinical decision-making, with internal validation being predominately used for model evaluation.

To effectively promote the extensive use of real-world data and drive a paradigm shift in clinical decision-making in China, multidisciplinary collaboration of key stakeholders is urgently needed.

Artificial intelligence has the potential to improve the care that is given to patients; however, the predictive models created are only as good as the base data used in their design. Perioperative blood management presents a complex clinical conundrum in which significant variability and the unstructured nature of the required data make it difficult to develop precise prediction models. There is a potential need for training clinicians to ensure they can interrogate the system and override when errors occur. Current systems created to predict perioperative blood transfusion are not generalizable across clinical settings, and there is a considerable cost implication required to research and develop artificial intelligence systems that would disadvantage resource-poor health systems. In addition, a lack of strong regulation currently means it is difficult to prevent bias.

INTRODUCTION:During future interplanetary space missions, a number of health conditions may arise, owing to the hostile environment of space and the myriad of stressors experienced by the crew. When managing these conditions, crews will be required to make accurate, timely clinical decisions at a high level of autonomy, as telecommunication delays and increasing distances restrict real-time support from the ground. On Earth, artificial intelligence (AI) has proven successful in healthcare, augmenting expert clinical decision-making or enhancing medical knowledge where it is lacking. Similarly, deploying AI tools in the context of a space mission could improve crew self-reliance and healthcare delivery.METHODS: We conducted a narrative review to discuss existing AI applications that could improve the prevention, recognition, evaluation, and management of the most mission-critical conditions, including psychological and mental health, acute radiation sickness, surgical emergencies, spaceflight-associated neuro-ocular syndrome, infections, and cardiovascular deconditioning.RESULTS: Some examples of the applications we identified include AI chatbots designed to prevent and mitigate psychological and mental health conditions, automated medical imaging analysis, and closed-loop systems for hemodynamic optimization. We also discuss at length gaps in current technologies, as well as the key challenges and limitations of developing and deploying AI for space medicine to inform future research and innovation. Indeed, shifts in patient cohorts, space-induced physiological changes, limited size and breadth of space biomedical datasets, and changes in disease characteristics may render the models invalid when transferred from ground settings into space.Cheung HC, De Louche C, Komorowski M. Artificial intelligence applications in space medicine. Aerosp Med Hum Perform. 2023; 94(8):610-622.

Although there has been no shortage of technological innovation in recent decades, a solution to sociodemographic disparities in the forensic setting has remained elusive. Artificial intelligence (AI) is a uniquely powerful emerging technology that is likely to either exacerbate or mitigate existing disparities and biases. This column argues that the implementation of AI in forensic settings is inevitable, and that practitioners and researchers should focus on developing AI systems that reduce bias and advance sociodemographic equity rather than attempt to impede the use of this novel technology.

The anesthesia machine serves as a vital piece of lifesaving equipment.

To analyze incidents of failures in the Primus anesthesia machine and address these malfunctions to reduce recurrence of failure, save maintenance costs, enhance safety, and improve overall efficiency.

We conducted an analysis on the records pertaining to the maintenance and parts replacement of the Primus anesthesia machines used in the Department of Anaesthesiology at Shanghai Chest Hospital over the past two years to identify the most common causes of failure. This included an assessment of the damaged parts and degree of damage, as well as a review of factors that caused the fault.

The main cause of the faults in the anesthesia machine was found to be air leakage and excessive humidity in the central air supply of the medical crane. The logistics department was instructed to increase inspections to check and ensure the quality of the central gas supply and ensure gas safety.

Summarizing the methods for dealing with anesthesia machine faults can save hospitals a lot of money, ensure normal hospital and department maintenance, and provide a reference to repair such faults. The use of Internet of Things platform technology can continuously develop the direction of digitalization, automation, and intelligent management in each stage of the "whole life cycle" of anesthesia machine equipment.

Physiological Closed-Loop Controlled systems continue to take a growing part in clinical practice, offering possibilities of providing more accurate, goal-directed care while reducing clinicians' cognitive and task load. These systems also provide a standardized approach for the clinical management of the patient, leading to a reduction in care variability across multiple dimensions. For fluid management and administration, the advantages of closed-loop technology are clear, especially in conditions that require precise care to improve outcomes, such as peri-operative care, trauma, and acute burn care. Controller design varies from simplistic to complex designs, based on detailed physiological models and adaptive properties that account for inter-patient and intra-patient variability; their maturity level ranges from theoretical models tested in silico to commercially available, FDA-approved products. This comprehensive scoping review was conducted in order to assess the current technological landscape of this field, describe the systems currently available or under development, and suggest further advancements that may unfold in the coming years. Ten distinct systems were identified and discussed.