Virtual reality in anesthesia "simulation"

Necessity and Importance of Developing AI in Anesthesia from the Perspective of Clinical Safety and Information Security

The rapid development of artificial intelligence (AI) technology is due to the significant progress in big data, databases, algorithms, and computing power, and medical research is a vital application direction of AI. The integrated development of AI and medicine has improved medical technology, and the efficiency of medical services and equipment has enabled doctors to better serve patients. The tasks and characteristics of the anesthesia discipline also make AI necessary for its development, and AI has also been initially applied in different fields of anesthesia. Our review aims to clarify the current situation and challenges of AI application in anesthesiology to provide clinical references and guide the future development of AI in anesthesiology. This review summarizes progress in the application of AI in perioperative risk assessment and prediction, deep monitoring and regulation of anesthesia, essential anesthesia skills operation, automatic drug administration systems, and teaching and training in anesthesia. Also discussed herein are the accompanying risks and challenges of applying AI in anesthesia: patient privacy and information security, data sources, and ethical issues, lack of capital and talent, and the "black box" phenomenon.

Artificial Intelligence and technology in COVID Era: A narrative review

Application of artificial intelligence (AI) in the medical field during the coronavirus disease 2019 (COVID-19) era is being explored further due to its beneficial aspects such as self-reported data analysis, X-ray interpretation, computed tomography (CT) image recognition, and patient management. This narrative review article included published articles from MEDLINE/PubMed, Google Scholar and National Informatics Center egov mobile apps. The database was searched for "Artificial intelligence" and "COVID-19" and "respiratory care unit" written in the English language during a period of one year 2019-2020. The relevance of AI for patients is in hands of people with digital health tools, Aarogya setu app and Smartphone technology. AI shows about 95% accuracy in detecting COVID-19-specific chest findings. Robots with AI are being used for patient assessment and drug delivery to patients to avoid the spread of infection. The pandemic outbreak has replaced the classroom method of teaching with the online execution of teaching practices and simulators. AI algorithms have been used to develop major organ tissue characterization and intelligent pain management techniques for patients. The Blue-dot AI-based algorithm helps in providing early warning signs. The AI model automatically identifies a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection sound pressure, and light level detection. There is now no looking back as AI and machine learning are to stay in the field of training, teaching, patient care, and research in the future.

Distance Education in Anesthesia Using Screen-Based Simulation - A Brief Integrative Review

Screen-based simulation (SBS) using digital technology has been demonstrated to improve the cognitive and psychomotor skills of anesthesia trainees. As a method of education and evaluation, this form of simulation offers multiple advantages related to cost, availability, simplicity, repeatability, and scorability. Online use of SBS with software employing standard cloud-based peer-to-peer platforms allows for instruction at a distance of important anesthesia-related critical thinking skills including crisis management. Despite the fact that there are no studies concerning the application of SBS in anesthesia distance education, this form of instruction has increased as a result of quarantine measures associated with the coronavirus-2 pandemic that have disrupted traditional in-person mannequin-based simulation, and its usage likely will continue through the post-pandemic era for multiple reasons. Several options exist for asynchronous and synchronous teaching of anesthesia skills at a distance with SBS, and there are useful techniques that can assist in achieving these educational goals with this process.

From Socrates to Virtual Reality: A Historical Review of Learning Theories and Their Influence on the Training of Anesthesiologists

Over the past couple of centuries, the training of American physicians, and anesthesiologists in particular, has undergone a radical transformation. The revolution of medical training has been and continues to be fueled by insights from learning theorists. In this historical review, we discuss the origins of American medical education in the 1700s and continue through the centuries illustrating the impact of learning theories on the education and training of anesthesiologists. In particular, we explore the impact of learning theories of the 1800s and the adult-centered teaching strategies of the 1900s. We also discuss the role of learning theories in molding medical education in the modern technological age.

Teaching Tip: Development of Veterinary Anesthesia Simulations for Pre-Clinical Training: Design, Implementation, and Evaluation Based on Student Perspectives

Anesthesia simulations have been used in pre-clinical medical training for decades to help learners gain confidence and expertise in an operating room environment without danger to a live patient. The authors describe a veterinary anesthesia simulation environment (VASE) with anesthesia scenarios developed to provide a re-creation of a veterinarian's task environment while performing anesthesia. The VASE uses advanced computer technology with simulator inputs provided from standard monitoring equipment in common use during veterinary anesthesia and a commercial canine training mannequin that allows intubation, ventilation, and venous access. The simulation outputs are determined by a script that outlines routine anesthesia scenarios and describes the consequences of students' hands-on actions and interventions during preestablished anesthetic tasks and critical incidents. Patients' monitored physiologic parameters may be changed according to predetermined learner events and students' interventions to provide immediate learner feedback and clinical realism. A total of 96 students from the pre-clinical anesthesia course participated in the simulations and the pre- and post-simulation surveys evaluating students' perspectives. Results of the surveys and comparisons of overall categorical cumulative responses in the pre- and post-simulation surveys indicated improvement in learners' perceived preparedness and confidence as a result of the simulated anesthesia experience, with significant improvement in the strongly agree, moderately agree, and agree categories (p<.05 at a 95% CI). These results suggest that anesthesia simulations in the VASE may complement traditional teaching methods through experiential learning and may help foster classroom-to-clinic transference of knowledge and skills without harm to an animal.