Vulnerable atherosclerotic plaque is a type of plaque that poses a significant risk of high mortality in patients with cardiovascular disease. Ultrasound has long been used for carotid atherosclerosis screening and plaque assessment due to its safety, low cost and non-invasive nature. However, conventional ultrasound techniques have limitations such as subjectivity, operator dependence, and low inter-observer agreement, leading to inconsistent and possibly inaccurate diagnoses. In recent years, a promising approach to address these limitations has emerged through the integration of artificial intelligence (AI) into ultrasound imaging. It was found that by training AI algorithms with large data sets of ultrasound images, the technology can learn to recognize specific characteristics and patterns associated with vulnerable plaques. This allows for a more objective and consistent assessment, leading to improved diagnostic accuracy. This article reviews the application of AI in the field of diagnostic ultrasound, with a particular focus on carotid vulnerable plaques, and discusses the limitations and prospects of AI-assisted ultrasound. This review also provides a deeper understanding of the role of AI in diagnostic ultrasound and promotes more research in the field.

The rapid integration of artificial intelligence (AI) into surgical practice necessitates a comprehensive evaluation of its applications, challenges, and physiological impact. This systematic review synthesizes current AI applications in surgery, with a particular focus on machine learning (ML) and its role in optimizing preoperative planning, intraoperative decision-making, and postoperative patient management. Using PRISMA guidelines and PICO criteria, we analyzed key studies addressing AI's contributions to surgical precision, outcome prediction, and real-time physiological monitoring. While AI has demonstrated significant promise-from enhancing diagnostics to improving intraoperative safety-many surgeons remain skeptical due to concerns over algorithmic unpredictability, surgeon autonomy, and ethical transparency. This review explores AI's physiological integration into surgery, discussing its role in real-time hemodynamic assessments, AI-guided tissue characterization, and intraoperative physiological modeling. Ethical concerns, including algorithmic opacity and liability in high-stakes scenarios, are critically examined alongside AI's potential to augment surgical expertise. We conclude that longitudinal validation, improved AI explainability, and adaptive regulatory frameworks are essential to ensure safe, effective, and ethically sound integration of AI into surgical decision-making. Future research should focus on bridging AI-driven analytics with real-time physiological feedback to refine precision surgery and patient safety strategies.

Aviation emissions significantly impact air quality, contributing to environmental degradation and public health risks. This study aims to assess the impact of aviation-related emissions on air quality at Alexandroupolis Regional Airport, Greece, and evaluate the role of meteorological factors in pollution dispersion. Using machine learning models, we analyzed emissions data, including CO2, NOx, CO, HC, SOx, PM2.5, fuel consumption, and meteorological parameters from 2019-2020. Results indicate that NOx and CO2 emissions showed the highest correlation with air traffic volume and fuel consumption (R = 0.63 and 0.67, respectively). Bayesian Linear Regression and Linear Regression emerged as the most accurate models, achieving an R2 value of 0.96 and 0.97, respectively, for predicting PM2.5 concentrations. Meteorological factors had a moderate influence, with precipitation negatively correlated with PM2.5 (-0.03), while temperature and wind speed showed limited effects on emissions. A significant decline in aviation emissions was observed in 2020, with CO2 emissions decreasing by 28.1%, NOx by 26.5%, and PM2.5 by 35.4% compared to 2019, reflecting the impact of COVID-19 travel restrictions. Carbon dioxide had the most extensive percentage distribution, accounting for 75.5% of total emissions, followed by fuels, which accounted for 24%, and the remaining pollutants, such as NOx, CO, HC, SOx, and PM2.5, had more minor impacts. These findings highlight the need for optimized air quality management at regional airports, integrating machine learning for predictive monitoring and supporting policy interventions to mitigate aviation-related pollution.

Background: While long-term opioid therapy is a widely utilized strategy for managing chronic pain, many patients have understandable questions and concerns regarding its safety, efficacy, and potential for dependency and addiction. Providing clear, accurate, and reliable information is essential for fostering patient understanding and acceptance. Generative artificial intelligence (AI) applications offer interesting avenues for delivering patient education in healthcare. This study evaluates the reliability, accuracy, and comprehensibility of ChatGPT's responses to common patient inquiries about opioid long-term therapy. Methods: An expert panel selected thirteen frequently asked questions regarding long-term opioid therapy based on the authors' clinical experience in managing chronic pain patients and a targeted review of patient education materials. Questions were prioritized based on prevalence in patient consultations, relevance to treatment decision-making, and the complexity of information typically required to address them comprehensively. We assessed comprehensibility by implementing the multimodal generative AI Copilot (Microsoft 365 Copilot Chat). Spanning three domains-pre-therapy, during therapy, and post-therapy-each question was submitted to GPT-4.0 with the prompt "If you were a physician, how would you answer a patient asking…". Ten pain physicians and two non-healthcare professionals independently assessed the responses using a Likert scale to rate reliability (1-6 points), accuracy (1-3 points), and comprehensibility (1-3 points). Results: Overall, ChatGPT's responses demonstrated high reliability (5.2 ± 0.6) and good comprehensibility (2.8 ± 0.2), with most answers meeting or exceeding predefined thresholds. Accuracy was moderate (2.7 ± 0.3), with lower performance on more technical topics like opioid tolerance and dependency management. Conclusions: While AI applications exhibit significant potential as a supplementary tool for patient education on opioid long-term therapy, limitations in addressing highly technical or context-specific queries underscore the need for ongoing refinement and domain-specific training. Integrating AI systems into clinical practice should involve collaboration between healthcare professionals and AI developers to ensure safe, personalized, and up-to-date patient education in chronic pain management.

While the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management.

This review was registered with PROSPERO (ID# CRD42022307017), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management.

From the 17,601 records identified in the initial search, 197 were included in this review. Identified applications of AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain, forecasting of individualized responses to treatment, treatment regimen tailoring, image-guidance for procedural interventions and self-management tools. Multiple domains of AI were used including machine learning, computer vision, fuzzy logic, natural language processing and expert systems.

There is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes. However, multiple barriers to their clinical integration remain including lack validation of such applications in diverse patient populations, missing infrastructure to support these tools and limited provider understanding of AI.

This review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum.

Brain tumors refer to the abnormal growths that occur within the brain's tissue, comprising both primary neoplasms and metastatic lesions. Timely detection, precise staging, suitable treatment, and standardized management are of significant clinical importance for extending the survival rates of brain tumor patients. Artificial intelligence (AI), a discipline within computer science, is leveraging its robust capacity for information identification and combination to revolutionize traditional paradigms of oncology care, offering substantial potential for precision medicine. This article provides an overview of the current applications of AI in brain tumors, encompassing the primary AI technologies, their working mechanisms and working workflow, the contributions of AI to brain tumor diagnosis and treatment, as well as the role of AI in brain tumor scientific research, particularly in drug innovation and revealing tumor microenvironment. Finally, the paper addresses the existing challenges, potential solutions, and the future application prospects. This review aims to enhance our understanding of the application of AI in brain tumors and provide valuable insights for forthcoming clinical applications and scientific inquiries.

Background/Objectives: Appropriate postoperative management, especially in pediatric patients, can be challenging for anesthesiologists. This retrospective study used machine learning to investigate the effects and complications of patient-controlled analgesia (PCA) in children undergoing orthopedic surgery. Methods: The medical records of children who underwent orthopedic surgery in a single tertiary hospital and received intravenous and epidural PCA were analyzed. Predictive models were developed using machine learning, and various demographic, anesthetic, and surgical factors were investigated to predict postoperative pain and complications associated with PCA. Results: Data from 1968 children were analyzed. Extreme gradient boosting effectively predicted moderate postoperative pain for the 6-24-h (area under curve (AUC): 0.85, accuracy (ACC): 0.79) and 24-48-h (AUC: 0.89, ACC: 0.87) periods after surgery. The factors that predicted moderate postoperative pain included the pain score immediately before the measurement period, the total amount of opioid infused, and age. For predicting side effects during the 6-24-h period after surgery, a least absolute shrinkage and selection operator model (AUC: 0.75, ACC: 0.64) was selected, while a random forest model (AUC: 0.91, ACC: 0.87) was chosen for the 24-48-h period post-surgery. The factors that predicted complications included the occurrence of side effects immediately before the measurement period, the total amount of opioid infused before the measurement period, and age. Conclusions: This retrospective study introduces machine-learning-based models and factors aimed at forecasting moderate postoperative pain and complications of PCA in children undergoing orthopedic surgery. This research has the potential to enhance postoperative pain management strategies for children.

Perioperative neurocognitive disorders (PNDs) are common complications after surgery and anaesthesia, particularly in older adults, leading to increased morbidity, mortality, and healthcare costs. Therefore, major medical societies have developed recommendations for the prevention and treatment of PNDs. Our study evaluated the reliability of large language models, specifically ChatGPT-4 and Gemini, in generating recommendations for PND management and comparing them with published guidelines.

We conducted an online cross-sectional web-based analysis over 48 h in June 2024. Artificial intelligence (AI)-generated recommendations were produced in six different locations across five countries (Switzerland, Belgium, Turkey, Canada, and the East and West Coasts of the USA). The English prompt 'a table of a bundle of care for perioperative neurocognitive disorders' was entered into ChatGPT-4 and Gemini, generating tables evaluated by independent reviewers. The primary outcomes were the Total Disagreement Score (TDS) and Quality Assessment of Medical Artificial Intelligence (QAMAI), which compared AI-generated recommendations with published guidelines.

The study generated 14 tables, with TDS and QAMAI scores showing similar results for ChatGPT-4 and Gemini (2 [1-3] vs 2 [2-3], P=0.636 and 4 [4-4] vs 4 [3-4], P=0.424, respectively). AI-generated recommendations aligned well with published guidelines, with the highest alignment observed in ChatGPT-4-generated recommendations. No complete agreement with guidelines was achieved, and lack of cited sources was a noted limitation.

Large language models can generate perioperative neurocognitive disorder recommendations that align closely with published guidelines. However, further validation and integration of clinician feedback are required before clinical application.

The use of artificial intelligence (AI) in obstetric anaesthesiology shows great potential in enhancing our practice and delivery of care. In this narrative review, we summarise the current applications of AI in four key areas of obstetric anaesthesiology (perioperative care, neuraxial procedures, labour analgesia and obstetric critical care), where AI has been employed to varying degrees for decision support, event prediction, risk stratification and procedural assistance. We also identify gaps in current practice and propose areas for further research. While promising, AI cannot replace the expertise and clinical judgement of a trained obstetric anaesthesiologist. It should, instead, be viewed as a valuable tool to facilitate and support our practice.

Acute coronary syndrome (ACS) is a leading cause of death worldwide. Prompt and accurate diagnosis of acute myocardial infarction (AMI) or ACS is crucial for improved management and prognosis of patients. The rapid growth of machine learning (ML) research has significantly enhanced our understanding of ACS. Most studies have focused on applying ML to detect ACS, predict prognosis, manage treatment, identify risk factors, and discover potential biomarkers, particularly using data from electrocardiograms (ECGs), electronic medical records (EMRs), imaging, and omics as the main data modality. Additionally, integrating ML with smart devices such as wearables, smartphones, and sensor technology enables real-time dynamic assessments, enhancing clinical care for patients with ACS. This review provided an overview of the workflow and key concepts of ML as they relate to ACS. It then provides an overview of current ML algorithms used for ACS diagnosis, prognosis, identification of potential risk biomarkers, and management. Furthermore, we discuss the current challenges faced by ML algorithms in this field and how they might be addressed in the future, especially in the context of medicine.

With an increase in geriatric population undergoing surgical procedures, research focused on enhancing their perioperative outcomes is of paramount importance. Currently, most of the evidence-based medicine protocols are driven by studies concentrating on adults encompassing all adult age groups. Given the alterations in physiology with aging, geriatric patients respond differently to anesthetics and, therefore, require specific research initiatives to further expound on the same. Large databases and the development of sophisticated analytic tools can provide meaningful insights into this. Here, we discuss a few research opportunities and challenges that data scientists face when focusing on geriatric perioperative research.

We aimed to explore the application value of unsupervised machine learning in identifying acute gastrointestinal injury (AGI) after extracorporeal circulation for acute type A aortic dissection (ATAAD).

Patients who underwent extracorporeal circulation for ATAAD at the First Hospital of Lanzhou University from January 2016 to January 2021 were included. Unsupervised machine learning algorithm was used to stratify patients into different phenogroups according to the similarity of their clinical features and laboratory test results. The differences in the incidence of perioperative AGI and other adverse events among different phenogroups were compared. Logistic regression was used to analyze the high-risk factors for AGI in each phenogroups and random forest (RF) algorithms were used to construct diagnostic models for AGI in different phenogroups.

A total of 188 patients were included, with 166 males and 22 females. Unsupervised Machine Learning stratified patients into three phenogroups (phenogroup A, B, and C). Compared with other phenogroups, phenogroup B patients were older (P < 0.01), had higher preoperative lactate and D-dimer levels, and had the highest incidence of AGI (52.5%, P < 0.001) and in-hospital mortality (18.6%, P = 0.002). The random forest model showed that the top four risk factors for AGI in phenogroup B were cardiopulmonary bypass time, operation time, aortic clamping time, and ventilator time, which were significantly different from other phenogroups. The areas under the curve (AUCs) for diagnosing postoperative AGI of phenogroup A, B, and C were 0.943 (0.854-0.992), 0.990 (0.966-1.000), and 0.964 (0.899-0.997) using the RF model, respectively.

Phenogroup stratification based on unsupervised learning can accurately identify high-risk populations for postoperative AGI in ATAAD, providing a new approach for implementing individualized preventive and therapeutic measures in clinical practice.

Intramuscular fat (IMF) is an important indicator for evaluating meat quality. Transcriptome sequencing (RNA-seq) is widely used for the study of IMF deposition. Machine learning (ML) is a new big data fitting method that can effectively fit complex data, accurately identify samples and genes, and it plays an important role in omics research. Therefore, this study aimed to analyze RNA-seq data by ML method to identify differentially expressed genes (DEGs) affecting IMF deposition in pigs. In this study, a total of 74 RNA-seq data from muscle tissue samples were used. A total of 155 DEGs were identified using a limma package between the two groups. 100 and 11 significant genes were identified by support vector machine recursive feature elimination (SVM-RFE) and random forest (RF) models, respectively. A total of six intersecting genes were in both models. KEGG pathway enrichment analysis of the intersecting genes revealed that these genes were enriched in pathways associated with lipid deposition. These pathways include α-linolenic acid metabolism, linoleic acid metabolism, ether lipid metabolism, arachidonic acid metabolism, and glycerophospholipid metabolism. Four key genes affecting intramuscular fat deposition, PLA2G6, MPV17, NUDT2, and ND4L, were identified based on significant pathways. The results of this study are important for the elucidation of the molecular regulatory mechanism of intramuscular fat deposition and the effective improvement of IMF content in pigs.

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) represents an exciting and evolving technology that is increasingly being utilized across pain medicine. Large language models (LLMs) are one type of AI that has become particularly popular. Currently, there is a paucity of literature analyzing the impact that AI may have on trainee education. As such, we sought to assess the benefits and pitfalls that AI may have on pain medicine trainee education. Given the rapidly increasing popularity of LLMs, we particularly assessed how these LLMs may promote and hinder trainee education through a pilot quality improvement project.

A comprehensive search of the existing literature regarding AI within medicine was performed to identify its potential benefits and pitfalls within pain medicine. The pilot project was approved by UPMC Quality Improvement Review Committee (#4547). Three of the most commonly utilized LLMs at the initiation of this pilot study - ChatGPT Plus, Google Bard, and Bing AI - were asked a series of multiple choice questions to evaluate their ability to assist in learner education within pain medicine.

Potential benefits of AI within pain medicine trainee education include ease of use, imaging interpretation, procedural/surgical skills training, learner assessment, personalized learning experiences, ability to summarize vast amounts of knowledge, and preparation for the future of pain medicine. Potential pitfalls include discrepancies between AI devices and associated cost-differences, correlating radiographic findings to clinical significance, interpersonal/communication skills, educational disparities, bias/plagiarism/cheating concerns, lack of incorporation of private domain literature, and absence of training specifically for pain medicine education. Regarding the quality improvement project, ChatGPT Plus answered the highest percentage of all questions correctly (16/17). Lowest correctness scores by LLMs were in answering first-order questions, with Google Bard and Bing AI answering 4/9 and 3/9 first-order questions correctly, respectively. Qualitative evaluation of these LLM-provided explanations in answering second- and third-order questions revealed some reasoning inconsistencies (e.g., providing flawed information in selecting the correct answer).

AI represents a continually evolving and promising modality to assist trainees pursuing a career in pain medicine. Still, limitations currently exist that may hinder their independent use in this setting. Future research exploring how AI may overcome these challenges is thus required. Until then, AI should be utilized as supplementary tool within pain medicine trainee education and with caution.

Patient reliance on internet resources for clinical information has steadily increased. The recent widespread accessibility of artificial intelligence (AI) tools like ChatGPT has increased patient reliance on these resources while also raising concerns about the accuracy, reliability, and appropriateness of the information they provide. Previous studies have evaluated ChatGPT and found it could accurately respond to questions on common surgeries, such as total hip arthroplasty, but is untested for uncommon procedures like total ankle arthroplasty (TAA). This study evaluates ChatGPT-4's performance in answering patient questions on TAA and further explores the opportunity for physician involvement in guiding the implementation of this technology.

Twelve commonly asked patient questions regarding TAA were collated from established sources and posed to ChatGPT-4 without additional input. Four fellowship-trained surgeons independently rated the responses using a 1-4 scale, assessing accuracy and need for clarification. Interrater reliability, divergence, and trends in response content were analyzed to evaluate consistency across responses.

The mean score across all responses was 1.8, indicating an overall satisfactory performance by ChatGPT-4. Ratings were consistently good on factual questions, such as infection risk and success rates, whereas questions requiring nuanced information, such as postoperative protocols and prognosis, received poorer ratings. Significant variability was observed among surgeons' ratings and between questions, reflecting differences in interpretation and expectations.

ChatGPT-4 demonstrates its potential to reliably provide discrete information for uncommon procedures such as TAA, but it lacks the capability to effectively respond to questions requiring patient- or surgeon-specific insight. This limitation, paired with the growing reliance on AI, highlights the need for AI tools tailored to specific clinical practices to enhance accuracy and relevance in patient education.

The application of artificial intelligence (AI) in anesthesiology has become increasingly widespread. However, no previous study has analyzed this field from the bibliometric analysis dimension. The objective of this paper was to assess the global research trends in AI in anesthesiology using bibliometric software. Literatures relevant to AI and anesthesiology were retrieved from the Web of Science until 10 April 2024 and were visualized and analyzed using Excel, CiteSpace, and VOSviewer. After screening, 491 studies were included in the final bibliometric analysis. The growth rate of publications, countries, institutions, authors, journals, literature co-citations, and keyword co-occurrences was computed. The number of publications increased annually since 2018, with the most significant contributions from the USA, China, and England. The top 3 institutions were Yuan Ze University, National Taiwan University, and Brunel University London. The top three journals were Anesthesia & Analgesia, BMC Anesthesiology, and the British Journal of Anaesthesia. The researches on the application of AI in predicting hypotension have been extensive and represented a hotspot and frontier. In terms of keyword co-occurrence cluster analysis, keywords were categorized into four clusters: ultrasound-guided regional anesthesia, postoperative pain and airway management, prediction, depth of anesthesia (DoA), and intraoperative drug infusion. This analysis provides a systematic analysis on the literature regarding the AI-related research in the field of anesthesiology, which may help researchers and anesthesiologists better understand the research trend of anesthesia-related AI.

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.

Glaucoma is a pathologically irreversible eye illness in the realm of ophthalmic diseases. Because it is difficult to detect concealed and non-obvious progressive changes, clinical diagnosis and treatment of glaucoma is extremely challenging. At the same time, screening and monitoring for glaucoma disease progression are crucial. Artificial intelligence technology has advanced rapidly in all fields, particularly medicine, thanks to ongoing in-depth study and algorithm extension. Simultaneously, research and applications of machine learning and deep learning in the field of glaucoma are fast evolving. Artificial intelligence, with its numerous advantages, will raise the accuracy and efficiency of glaucoma screening and diagnosis to new heights, as well as significantly cut the cost of diagnosis and treatment for the majority of patients. This review summarizes the relevant applications of artificial intelligence in the screening and diagnosis of glaucoma, as well as reflects deeply on the limitations and difficulties of the current application of artificial intelligence in the field of glaucoma, and presents promising prospects and expectations for the application of artificial intelligence in other eye diseases such as glaucoma.

Deep learning has revolutionized cancer diagnostics, shifting from pixel-based image analysis to more comprehensive, patient-centric care. This opinion article explores recent advancements in neural network architectures, highlighting their evolution in biomedical research and their impact on medical imaging interpretation and multimodal data integration. We emphasize the need for domain-specific artificial intelligence (AI) systems capable of handling complex clinical tasks, advocating for the development of multimodal large language models that can integrate diverse data sources. These models have the potential to significantly enhance the precision and efficiency of cancer diagnostics, transforming AI from a supplementary tool into a core component of clinical decision-making, ultimately improving patient outcomes and advancing cancer care.

General anesthesia typically involves three key components: amnesia, analgesia, and immobilization. Monitoring the depth of anesthesia (DOA) during surgery is crucial for personalizing anesthesia regimens and ensuring precise drug delivery. Since general anesthetics act primarily on the brain, this organ becomes the target for monitoring DOA. Electroencephalogram (EEG) can record the electrical activity generated by various brain tissues, enabling anesthesiologists to monitor the DOA from real-time changes in a patient's brain activity during surgery. This monitoring helps to optimize anesthesia medication, prevent intraoperative awareness, and reduce the incidence of cardiovascular and other adverse events, contributing to anesthesia safety. Different anesthetic drugs exert different effects on the EEG characteristics, which have been extensively studied in commonly used anesthetic drugs. However, due to the limited understanding of the biological basis of consciousness and the mechanisms of anesthetic drugs acting on the brain, combined with the effects of various factors on existing EEG monitors, DOA cannot be accurately expressed via EEG. The lack of patient reactivity during general anesthesia does not necessarily indicate unconsciousness, highlighting the importance of distinguishing the mechanisms of consciousness and conscious connectivity when monitoring perioperative anesthesia depth. Although EEG is an important means of monitoring DOA, continuous optimization is necessary to extract characteristic information from EEG to monitor DOA, and EEG monitoring technology based on artificial intelligence analysis is an emerging research direction.

Patient safety in anesthesiology has advanced significantly over the past several decades. The current process of improving care is often based on studying adverse events (AEs) and near misses. However, there is a wealth of information not captured by focusing solely on these events, potentially resulting in missed opportunities for care improvements.

We review terms such as AEs and nonroutine events (NREs), and introduce the concept of unanticipated events (UEs), defined as events that deviate from intended care that may/may not have been caused by error, may/may not be preventable, and may/may not have caused injury to a patient. UEs incorporate AEs in addition to many other anesthetic events not routinely tracked, allowing for trend analysis over time and the identification of additional opportunities for quality improvement. We review both automated and self-reporting tools that currently exist to capture this often-neglected wealth of data. Finally, we discuss the responsibility of quality/safety leaders for data monitoring.

Consistent reporting and monitoring for trends related to UEs could allow departments to identify risks and mitigate harm before it occurs. We review various proposed methods to expand data collection, and recommend anesthesia practices pursue UE tracking through department-specific reporting interfaces.

The electronic health record (EHR) is an invaluable tool that may be used to improve patient safety. With a variety of different features, such as clinical decision support and computerized physician order entry, it has enabled improvement of patient care throughout medicine. EHR allows for built-in reminders for such items as antibiotic dosing and venous thromboembolism prophylaxis.

In anesthesiology, EHR often improves patient safety by eliminating the need for reliance on manual documentation, by facilitating information transfer and incorporating predictive models for such items as postoperative nausea and vomiting. The use of EHR has been shown to improve patient safety in specific metrics such as using checklists or information transfer amongst clinicians; however, limited data supports that it reduces morbidity and mortality.

There are numerous potential pitfalls associated with EHR use to improve patient safety, as well as great potential for future improvement.

Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) presents a significant challenge for anesthesiologists. Machine Learning (ML)-assisted tools have the potential to enhance the recognition of patients at risk of complications and predict potential issues, ultimately improving outcomes.

We evaluated the prediction capacity of six models, ranging from logistic regression to support vector machine, using a dataset comprising 33 variables and 1364 subjects. The Area Under the Curve (AUC) and the F1 score served as the primary evaluation metrics. Our primary objectives were twofold: first, to develop an effective prediction model, and second, to create a user-friendly comprehensive model for identifying high-risk patients.

The logistic regression model demonstrated the highest effectiveness, achieving an AUC of 83.65%, and an F1 score of 0.7296, with balanced sensitivity and specificity of 77.94% and 76.47%, respectively. In comparison, the comprehensive three-layer decision tree model achieved an AUC of 72.84%, with sensitivity (79.41%) comparable to more complex models.

Our machine learning-assisted tools provide an additional perspective and enhance the predictive capabilities of traditional scoring methods. These tools can assist anesthesiologists in making well-informed decisions. Furthermore, we have successfully demonstrated the feasibility of creating a practical white-box model. The next steps involve conducting clinical validation and multicenter cross-validation.

NCT05537168.

The medical metaverse is a combination of medicine, computer science, information technology and other cutting-edge technologies. It redefines the method of information interaction about doctor-patient communication, medical education and research through the integration of medical data, knowledge and services in a virtual environment. Artificial intelligence (AI) is a discipline that uses computer technology to study and develop human intelligence. AI has infiltrated every aspect of medical metaverse and is deeply integrated with the technologies that build medical metaverse, such as large language models (LLMs), digital twins, blockchain and extended reality (including VR/AR/XR). AI has become an integral part of the medical metaverse building process. Moreover, AI also provides richer medical metaverse functions, including diagnosis, education, and consulting. This paper aims to introduce how AI supports the development of medical metaverse, including its specific application scenarios, shortcomings and future development. Our goal is to contribute to the advancement of more sophisticated and intelligent medical methods.

We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes.

Ovid MEDLINE, CINAHL, Embase, Scopus, PubMed, and ClinicalTrials.gov were searched to identify randomised controlled trials (RCTs) evaluating the effectiveness of ML-driven interventions in surgical inpatient populations. The review was registered with PROSPERO (CRD42023433163) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analysis was conducted for outcomes with two or more studies using a random-effects model, and vote counting was conducted for other outcomes.

Among 13 included RCTs, three types of ML-driven interventions were evaluated: Hypotension Prediction Index (HPI) (n=5), Nociception Level Index (NoL) (n=7), and a scheduling system (n=1). Compared with the standard care, HPI led to a significant decrease in absolute hypotension (n=421, P=0.003, I2=75%) and relative hypotension (n=208, P<0.0001, I2=0%); NoL led to significantly lower mean pain scores in the post-anaesthesia care unit (PACU) (n=191, P=0.004, I2=19%). NoL showed no significant impact on intraoperative opioid consumption (n=339, P=0.31, I2=92%) or PACU opioid consumption (n=339, P=0.11, I2=0%). No significant difference in hospital length of stay (n=361, P=0.81, I2=0%) and PACU stay (n=267, P=0.44, I2=0) was found between HPI and NoL.

HPI decreased the duration of intraoperative hypotension, and NoL decreased postoperative pain scores, but no significant impact on other clinical outcomes was found. We highlight the need to address both methodological and clinical practice gaps to ensure the successful future implementation of ML-driven interventions.

CRD42023433163 (PROSPERO).

The use of AI in the healthcare sector is facing some formidable concerns raised by the practitioners themselves. This study aimed to establish the concerns that surround the adoption of AI among Saudi Arabian healthcare professionals. Materials and methods: This was a cross-sectional study using stratified convenience sampling from September to November 2024 across health facilities. This study included all licensed healthcare professionals practicing for at least one year, whereas interns and administrative staff were excluded from the research. Data collection was conducted through a 33-item validated questionnaire that was provided in paper form and online. The questionnaire measured AI awareness with eight items, past experience with five items, and concerns in four domains represented by 20 items. Four hundred questionnaires were distributed, and the response rate was 78.5% (n = 314). The majority of the participants were females (52.5%), Saudis (89.2%), and employees of MOH (77.1%). The mean age for the participants was 35.6 ± 7.8 years. Quantitative analysis revealed high AI awareness scores with a mean of 3.96 ± 0.167, p < 0.001, and low previous experience scores with a mean of 2.65 ± 0.292. Data management-related worries came out as the top worry, with a mean of 3.78 ± 0.259, while the poor data entry impact topped with a mean of 4.15 ± 0.801; healthcare provider-related worries with a mean of 3.71 ± 0.182; and regulation/ethics-related worries with a mean of 3.67 ± 0.145. Health professionals' main concerns about AI adoption were related to data reliability and impacts on clinical decision-making, which significantly hindered successful AI integration in healthcare. These are the particular concerns that, if addressed through robust data management protocols and enhanced processes for clinical validation, will afford the best implementation of AI technology in an optimized way to bring better quality and safety to healthcare. Quantitative validation of AI outcomes and the development of standardized integration frameworks are subjects for future research.

Breast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S. are projected to receive an invasive breast cancer diagnosis, alongside 56,500 cases of ductal carcinoma in situ (DCIS). Breast cancer occurs in every country of the world in women at any age after puberty but with increasing rates in later life. About 65% of women with the BRCA1 and 45% with the BRCA2 gene variants develop breast cancer by age 70. While these genes account for 5% of breast cancers, their prevalence is higher in certain populations. Advances in early detection, personalised medicine, and AI-driven diagnostics are improving outcomes by enabling a more precise analysis, reducing recurrence, and minimising treatment side effects. Our paper aims to explore the vast applications of artificial intelligence within the diagnosis and treatment of breast cancer and how these advancements can contribute to elevating patient care as well as discussing the potential drawbacks of such integrations into modern medicine. We structured our paper as a non-systematic review and utilised Google Scholar and PubMed databases to review literature regarding the incorporation of AI in the diagnosis and treatment of non-palpable breast masses. AI is revolutionising breast cancer management by enhancing imaging, pathology, and personalised treatment. In imaging, AI can improve the detection of cancer in mammography, MRIs, and ultrasounds, rivalling expert radiologists in accuracy. In pathology, AI enhances biomarker detection, improving HER2 and Ki67 assessments. Personalised medicine benefits from AI's predictive power, aiding risk stratification and treatment response. AI also shows promise in triple-negative breast cancer management, offering better prognosis and subtype classification. However, challenges include data variability, ethical concerns, and real-world validation. Despite limitations, AI integration offers significant potential in improving breast cancer diagnosis, prognosis, and treatment outcomes.

The rapid rise and potential of artificial intelligence (AI) have created growing excitement and much debate on its potential to bring transformative changes across entire industries, including the medical industry. This systematic review aims to investigate the advancements in the AI industry and its potential implementation, specifically in the field of anaesthesiology. AI has already been integrated into different areas of medicine, including diagnostic uses in radiology and pathology and therapeutic and interventional uses in cardiology and surgery. In the field of anaesthesiology, AI has made significant progress. Potential applications include personalised drug dosing, real-time monitoring of vital signs, automated anaesthesia delivery systems, and predictive analytics for adverse events. As AI technologies continue to advance and become more prevalent in medicine, clinicians across all specialities need to understand these technologies and how they can be utilised to provide safer and more efficient care. With the rapid evolution of AI and the introduction of new concepts such as machine learning (ML), deep learning (DL), and neural networks, the field of anaesthesiology is set to undergo transformative changes. In this systematic review, we examine the existing literature to explore the current state of AI in the field of anaesthesiology, along with a prospective look at potential applications in the future. Along with its various applications, we will also discuss its limitations and flaws. As the field progresses, it is crucial to thoughtfully examine the ethical aspects of using AI in anaesthesia and ensure these technologies are applied responsibly and transparently.

Artificial intelligence (AI) is transforming ophthalmology by leveraging machine learning (ML) and deep learning (DL) techniques, particularly artificial neural networks (ANN) and convolutional neural networks (CNN) to mimic human brain functions and enhance accuracy through data exposure. These AI systems are particularly effective in analyzing ophthalmic images for early disease detection, improving diagnostic precision, streamlining clinical workflows, and ultimately enhancing patient outcomes. This study aims to explore the specific applications and impact of AI in the fields of glaucoma, corneal diseases, and oculoplastics. This study reviews current AI technologies in ophthalmology, examining the implementation of ML and DL techniques. It evaluates AI's role in early disease detection, diagnostic accuracy, clinical workflow enhancement, and patient outcomes. AI has significantly advanced the early detection and management of various ocular conditions. In glaucoma, AI systems provide standardized, rapid identification of disease characteristics, reducing intra- and interobserver bias and workload. For corneal diseases, AI tools enhance diagnostic methods for conditions such as keratitis and keratoconus, improving early detection and treatment planning. In oculoplastics, AI assists in the diagnosis and monitoring of eyelid and orbital diseases, facilitating precise surgical planning and postoperative management. The integration of AI in ophthalmology has revolutionized eye care by enhancing diagnostic precision, streamlining clinical workflows, and improving patient outcomes. As AI technologies continue to evolve, their applications in ophthalmology are expected to expand, offering innovative solutions for the diagnosis, monitoring, treatment, and surgical outcomes of various eye conditions.

Artificial intelligence (AI) is one of the sectors of medical research that is expanding the fastest right now in healthcare. AI has rapidly advanced in the field of medicine, helping to treat a variety of illnesses and reducing the number of diagnostic and follow-up errors.

This study aims to assess the perception and attitude towards artificial intelligence (AI) among medical students & house officers in Egypt.

An online cross-sectional study was done using a questionnaire on the Google Form website. The survey collected demographic data and explored participants' perception, attitude & potential barriers towards AI.

There are 1,346 responses from Egyptian medical students (25.8%) & house officers (74.2%). Most participants have inadequate perception (76.4%) about the importance and usage of AI in the medical field, while the majority (87.4%) have a negative attitude. Multivariate analysis revealed that age is the only independent predictor of AI perception (AOR = 1.07, 95% CI 1.01-1.13). However, perception level and gender are both independent predictors of attitude towards AI (AOR = 1.93, 95% CI 1.37-2.74 & AOR = 1.80, 95% CI 1.30-2.49, respectively).

The study found that medical students and house officers in Egypt have an overall negative attitude towards the integration of AI technologies in healthcare. Despite the potential benefits of AI-driven digital medicine, most respondents expressed concerns about the practical application of these technologies in the clinical setting. The current study highlights the need to address the concerns of medical students and house officers towards AI integration in Egypt. A multi-pronged approach, including education, targeted training, and addressing specific concerns, is necessary to facilitate the wider adoption of AI-enabled healthcare.

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Recent studies have highlighted the significant role of cerebrospinal fluid (CSF) in reflecting pathophysiological PD brain conditions by analyzing the components of CSF. Based on the published literature, we created a single network with altered metabolites in the CSF of patients with PD. We analyzed biological functions related to the transmembrane of mitochondria, respiration of mitochondria, neurodegeneration, and PD using a bioinformatics tool. As the proteome reflects phenotypes, we collected proteome data based on published papers, and the biological function of the single network showed similarities with that of the metabolomic network. Then, we analyzed the single network of integrated metabolome and proteome. In silico predictions based on the single network with integrated metabolomics and proteomics showed that neurodegeneration and PD were predicted to be activated. In contrast, mitochondrial transmembrane activity and respiration were predicted to be suppressed in the CSF of patients with PD. This review underscores the importance of integrated omics analyses in deciphering PD's complex biochemical networks underlying neurodegeneration.

The use of Artificial Intelligence (AI) relies on computer science and large datasets, with the technology mimicking human intelligence as it makes logical decisions. This study aims to assess the perceptions and experiences of anesthesiology practitioners toward AI and identify its benefits to healthcare professionals and patients, along with current and future applications of AI.

This cross-sectional descriptive online survey study was disseminated to physicians who work in anesthesiology practice in Saudi Arabia. Descriptive statistics were used to report the characteristics of the respondents and summarize the results of the survey.

There were 109 responses, with 85.32% being male, 35.78% being aged 40-49 years, and 69.72% being consultant anesthesiologists. The majority of participants (73.39%) believed that AI could be used in multiple settings related to anesthesiology practice. Participants also believed that AI could facilitate access to data (76.15%), enable precise decision-making (75.23%), reduce medical errors (55.04%), reduce workload and shortage of healthcare personnel (53.21%), and allow healthcare personnel to focus on more demanding cases (69.72%). In addition, the majority of participants believed that AI can be beneficial to patients, in which 69.72% believed that AI can improve patient access to care, 77.06% believed that AI can facilitate patient education, and 65.14% believed that AI can guide patients during treatment. Lastly, 70.64% believed that AI would be beneficial to anesthesiology practices in the future. However, 61.47% claimed that their workplace has no plan for adopting AI.

The anesthesiologists showed generally positive attitudes towards AI, in spite of its limited utilization and implementation challenges. Strong beliefs exist about AI's future potential in anesthesia care and postgraduate education.

Use of artificial intelligence (AI) is expanding exponentially as it pertains to workflow operations. Otolaryngology-Head and Neck Surgery (OHNS), as with all medical fields, is just now beginning to realize the exciting upsides of AI as it relates to patient care but otolaryngologists should also be critical when considering using AI solutions. This paper highlights how AI can optimize clinical workflows in the outpatient, inpatient, and surgical settings while also discussing some of the possible drawbacks with the burgeoning technology.

Philips Visual Patient Avatar is an innovative approach to patient monitoring. Computer-based simulation studies have shown that it can improve diagnostic accuracy and confidence while reducing perceived workload. Following its integration into clinical practice, we conducted a single-centre qualitative study at the University Hospital Zurich to explore the views of anaesthesia, post-anaesthesia and intensive care providers on their experience with the technology. We used an online survey to assess its contributions in different clinical situations. We analysed the data thematically to identify key themes. Of the 510 healthcare providers contacted, 131 (25.7%) completed the survey and 154 comments were collected. Key themes included the detection of specific vital sign changes, focusing on temperature and oxygen saturation (41.9%, 34/81 comments in the operating room; 38.6%, 17/44 comments in the intensive care unit; 10.3%, 3/29 comments in the post-anaesthesia care unit). Additionally, the technology was perceived to support daily routines and situational awareness (28.4%, 23/81 comments in the OR; 9.1%, 4/44 comments in the ICU; 10.3%, 3/29 comments in the PACU). The study provides early, but strong evidence that the Philips Visual Patient Avatar assists healthcare providers in specific clinical situations in the perioperative and critical care settings.

Establishing a convolutional neural network model for the recognition of characteristic raw electroencephalogram (EEG) signals is crucial for monitoring consciousness levels and guiding anesthetic drug administration.

This trial was conducted from December 2023 to March 2024. A total of 40 surgery patients were randomly divided into either a propofol group (1% propofol injection, 10 mL: 100 mg) (P group) or a propofol-etomidate combination group (1% propofol injection, 10 mL: 100 mg, and 0.2% etomidate injection, 10 mL: 20 mg, mixed at a 2:1 volume ratio) (EP group). In the P group, target-controlled infusion (TCI) was employed for sedation induction, with an initial effect site concentration set at 5-6 μg/mL. The EP group received an intravenous push with a dosage of 0.2 mL/kg. Six consciousness-related EEG features were extracted from both groups and analyzed using four prediction models: support vector machine (SVM), Gaussian Naive Bayes (GNB), artificial neural network (ANN), and one-dimensional convolutional neural network (1D CNN). The performance of the models was evaluated based on accuracy, precision, recall, and F1-score.

The power spectral density (94%) and alpha/beta ratio (72%) demonstrated higher accuracy as indicators for assessing consciousness. The classification accuracy of the 1D CNN model for anesthesia-induced unconsciousness (97%) surpassed that of the SVM (83%), GNB (81%), and ANN (83%) models, with a significance level of p < 0.05. Furthermore, the mean and mean difference ± standard error of the primary power values for the EP and P groups during the induced period were as follows: delta (23.85 and 16.79, 7.055 ± 0.817, p < 0.001), theta (10.74 and 8.743, 1.995 ± 0.7045, p < 0.02), and total power (24.31 and 19.72, 4.588 ± 0.7107, p < 0.001).

Large slow-wave oscillations, power spectral density, and the alpha/beta ratio are effective indicators of changes in consciousness during intravenous anesthesia with a propofol-etomidate combination. These indicators can aid anesthesiologists in evaluating the depth of anesthesia and adjusting dosages accordingly. The 1D CNN model, which incorporates consciousness-related EEG features, represents a promising tool for assessing the depth of anesthesia.

https://www.chictr.org.cn/index.html.

The rapid advancement of new technologies has resulted in a surge of data, while conventional computers are nearing their computational limits. The prevalent von Neumann architecture, where processing and storage units operate independently, faces challenges such as data migration through buses, leading to decreased computing speed and increased energy loss. Ongoing research aims to enhance computing capabilities through the development of innovative chips and the adoption of new system architectures. One noteworthy advancement is Resistive Random Access Memory (RRAM), an emerging memory technology. RRAM can alter its resistance through electrical signals at both ends, retaining its state even after power-down. This technology holds promise in various areas, including logic computing, neural networks, brain-like computing, and integrated technologies combining sensing, storage, and computing. These cutting-edge technologies offer the potential to overcome the performance limitations of traditional architectures, significantly boosting computing power. This discussion explores the physical mechanisms, device structure, performance characteristics, and applications of RRAM devices. Additionally, we delve into the potential future adoption of these technologies at an industrial scale, along with prospects and upcoming research directions.

Integration of artificial intelligence (AI) into medical practice has increased recently. Numerous AI models have been developed in the field of anesthesiology; however, their use in clinical settings remains limited. This study aimed to identify the gap between AI research and its implementation in anesthesiology via a systematic review of randomized controlled trials with meta-analysis (CRD42022353727).

We searched the databases of Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), Institute of Electrical and Electronics Engineers Xplore (IEEE), and Google Scholar and retrieved randomized controlled trials comparing conventional and AI-assisted anesthetic management published between the date of inception of the database and August 31, 2023.

Eight randomized controlled trials were included in this systematic review (n = 568 patients), including 286 and 282 patients who underwent anesthetic management with and without AI-assisted interventions, respectively. AI-assisted interventions used in the studies included fuzzy logic control for gas concentrations (one study) and the Hypotension Prediction Index (seven studies; adding only one indicator). Seven studies had small sample sizes (n = 30 to 68, except for the largest), and meta-analysis including the study with the largest sample size (n = 213) showed no difference in a hypotension-related outcome (mean difference of the time-weighted average of the area under the threshold 0.22, 95% confidence interval -0.03 to 0.48, P = 0.215, I2 93.8%).

This systematic review and meta-analysis revealed that randomized controlled trials on AI-assisted interventions in anesthesiology are in their infancy, and approaches that take into account complex clinical practice should be investigated in the future.

This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022353727).

Artificial intelligence and large language models (LLMs) have emerged as potentially disruptive technologies in healthcare. In this study GPT-3.5, an accessible LLM, was assessed for its accuracy and reliability in performing guideline-based evaluation of neuraxial bleeding risk in hypothetical patients on anticoagulation medication. The study also explored the impact of structured prompt guidance on the LLM's performance.

A dataset of 10 hypothetical patient stems and 26 anticoagulation profiles (260 unique combinations) was developed based on American Society of Regional Anesthesia and Pain Medicine guidelines. Five prompts were created for the LLM, ranging from minimal guidance to explicit instructions. The model's responses were compared with a "truth table" based on the guidelines. Performance metrics, including accuracy and area under the receiver operating curve (AUC), were used.

Baseline performance of GPT-3.5 was slightly above chance. With detailed prompts and explicit guidelines, performance improved significantly (AUC 0.70, 95% CI (0.64 to 0.77)). Performance varied among medication classes.

LLMs show potential for assisting in clinical decision making but rely on accurate and relevant prompts. Integration of LLMs should consider safety and privacy concerns. Further research is needed to optimize LLM performance and address complex scenarios. The tested LLM demonstrates potential in assessing neuraxial bleeding risk but relies on precise prompts. LLM integration should be approached cautiously, considering limitations. Future research should focus on optimization and understanding LLM capabilities and limitations in healthcare.

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 aim of this study was to understand the trends regarding the use of artificial intelligence in dentistry through a bibliometric review.

The authors performed a literature search on Web of Science. They collected the following data: articles-number and density of citations, year, key words, language, document type, study design, and theme (main objective, diagnostic method, and specialties); journals-impact factor; authors-country, continent, and institution. The authors used Visualization of Similarities Viewer software (Leiden University) to analyze the data and Spearman test for correlation analysis.

After selection, 1,478 articles were included. The number of citations ranged from 0 through 327. The articles were published from 1984 through 2024. Most articles were characterized as proof of concept (979). Definition and classification of structures and diseases was the most common theme (550 articles). There was an emphasis on radiology (333 articles) and radiographic-based diagnostic methods (715 articles). China was the country with the most articles (251), and Asia was the continent with the most articles (871). The Charité-University of Medicine Berlin was the institution with the most articles (42), and the author with the most articles was Schwendicke (53).

Artificial intelligence is an important clinical tool to facilitate diagnosis and provide automation in various processes.

The rapidly developing field of artificial intelligence (AI) may soon equip clinicians with algorithms that model and predict perioperative problems with extreme accuracy. Here, we outline emerging AI applications in preoperative risk stratification and intraoperative event prediction, where algorithm performance has been shown to outstrip commonly used conventional risk prediction tools. While offering an enticing view of a novel perioperative practice with superhuman foresight, AI's limited scope and lack of transparency remain key challenges for widespread adoption. As yet it is unclear whether machine learning alone can influence human clinical practice to exert real-world effects on patient outcomes.

Disparities in patient care and outcomes are well-documented in medicine but have received comparatively less attention in anesthesiology. Those disparities linked to racial and ethnic identity are pervasive, with compelling evidence in operative anesthesiology, obstetric anesthesiology, pain medicine, and critical care. This narrative review presents an overview of disparities in perioperative patient care that is grounded in historical context followed by potential solutions for mitigating disparities and inequities.

Effective training in regional anaesthesia (RA) is paramount to ensuring widespread competence. Technology-based learning has assisted other specialties in achieving more rapid procedural skill acquisition. If applicable to RA, technology-enhanced training has the potential to provide an effective learning experience and to overcome barriers to RA training. We review the current evidence base for use of innovative technologies in assisting learning of RA.

Using scoping review methodology, three databases (MEDLINE, Embase, and Web of Science) were searched, identifying 158 relevant citations. Citations were screened against defined eligibility criteria with 27 studies selected for inclusion. Data relating to study details, technological learning interventions, and impact on learner experience were extracted and analysed.

Seven different technologies were used to train learners in RA: artificial intelligence, immersive virtual reality, desktop virtual reality, needle guidance technology, robotics, augmented reality, and haptic feedback devices. Of 27 studies, 26 reported a positive impact of technology-enhanced RA training, with different technologies offering benefits for differing components of RA training. Artificial intelligence improved sonoanatomical knowledge and ultrasound skills for RA, whereas needle guidance technologies enhanced confidence and improved needling performance, particularly in novices. Immersive virtual reality allowed more rapid acquisition of needling skills, but its functionality was limited when combined with haptic feedback technology. User friendly technologies enhanced participant experience and improved confidence in RA; however, limitations in technology-assisted RA training restrict its widespread use.

Technology-enhanced RA training can provide a positive and effective learning experience, with potential to reduce the steep learning curve associated with gaining RA proficiency. A combined approach to RA education, using both technological and traditional approaches, should be maintained as no single method has been shown to provide comprehensive RA training.

Introduction Artificial intelligence (AI) has been gaining considerable attention in recent years within the healthcare field. It has established a presence in various aspects of health sciences, including accurate diagnosis and precise, streamlined treatment. This study aimed to assess the attitudes of dental residents and dentists in the Navi Mumbai region toward the use of AI in dentistry. Methods An online questionnaire-based survey was conducted, inviting 130 dental residents and dentists from the Navi Mumbai region. The collected data were compiled on a worksheet and subjected to descriptive statistical tests, which were expressed in numbers and frequencies. Results A total of 100 responses were received. Sixty-eight percent of individuals agreed that AI helps enhance diagnosis and treatment planning in the dental field. Sixty-five percent of the respondents stated that they are most likely to incorporate AI tools into their practice within the next five years. Conclusion From the present study, it can be inferred that AI is a promising and essential subsidiary tool in dentistry as well as in healthcare as a whole. However, major concerns such as extensive, in-depth training, data security, and cybercrime must be addressed before the full-scale incorporation of AI in the health sciences.