Examining the impact perceptual learning artificial-intelligence-based on the incidence of paresthesia when performing the ultrasound-guided popliteal sciatic block: simulation-based randomized study

Educational outcomes of simulation-based training in regional anaesthesia: a scoping review

BACKGROUND

Structured training in regional anaesthesia includes pretraining on simulation-based educational platforms to establish a safe and controlled learning environment before learners are provided clinical exposure in an apprenticeship model. This scoping review was designed to appraise the educational outcomes of current simulation-based educational modalities in regional anaesthesia.

METHODS

This review conformed to PRISMA-ScR guidelines. Relevant articles were searched in PubMed, Scopus, Google Scholar, Web of Science, and EMBASE with no date restrictions, until November 2023. Studies included randomised controlled trials, pre-post intervention, time series, case control, case series, and longitudinal studies, with no restrictions to settings, language or ethnic groups. The Kirkpatrick framework was applied for extraction of educational outcomes.

RESULTS

We included 28 studies, ranging from 2009 to 2023, of which 46.4% were randomised controlled trials. The majority of the target population was identified as trainees or residents (46.4%). Higher order educational outcomes that appraised translation to real clinical contexts (Kirkpatrick 3 and above) were reported in 12 studies (42.9%). Two studies demonstrated translational patient outcomes (Level 4) with reduced incidence of paraesthesia and clinical complications. The majority of studies appraised Level 3 outcomes of performance improvements in either laboratory simulation contexts (42.9%) or demonstration of clinical performance improvements in regional anaesthesia (39.3%).

CONCLUSIONS

There was significant heterogeneity in the types of simulation modalities used, teaching interventions applied, study methodologies, assessment tools, and outcome measures studied. When improvisations were made to regional anaesthesia simulation platforms (hybrid simulation), there were sustained educational improvements beyond 6 months. Newer technology-enhanced innovations such as virtual, augmented, and mixed reality simulations are evolving, with early reports of educational effectiveness.

Artificial intelligence and robotics in regional anesthesia

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.

The educational impact of technology-enhanced learning in regional anaesthesia: a scoping review

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines

BACKGROUND

Artificial intelligence (AI) for ultrasound scanning in regional anaesthesia is a rapidly developing interdisciplinary field. There is a risk that work could be undertaken in parallel by different elements of the community but with a lack of knowledge transfer between disciplines, leading to repetition and diverging methodologies. This scoping review aimed to identify and map the available literature on the accuracy and utility of AI systems for ultrasound scanning in regional anaesthesia.

METHODS

A literature search was conducted using Medline, Embase, CINAHL, IEEE Xplore, and ACM Digital Library. Clinical trial registries, a registry of doctoral theses, regulatory authority databases, and websites of learned societies in the field were searched. Online commercial sources were also reviewed.

RESULTS

In total, 13,014 sources were identified; 116 were included for full-text review. A marked change in AI techniques was noted in 2016-17, from which point on the predominant technique used was deep learning. Methods of evaluating accuracy are variable, meaning it is impossible to compare the performance of one model with another. Evaluations of utility are more comparable, but predominantly gained from the simulation setting with limited clinical data on efficacy or safety. Study methodology and reporting lack standardisation.

CONCLUSIONS

There is a lack of structure to the evaluation of accuracy and utility of AI for ultrasound scanning in regional anaesthesia, which hinders rigorous appraisal and clinical uptake. A framework for consistent evaluation is needed to inform model evaluation, allow comparison between approaches/models, and facilitate appropriate clinical adoption.

Applications of Artificial Intelligence in Pain Medicine

PURPOSE OF REVIEW

This review explores the current applications of artificial intelligence (AI) in the field of pain medicine with a focus on machine learning.

RECENT FINDINGS

Utilizing a literature search conducted through the PubMed database, several current trends were identified, including the use of AI as a tool for diagnostics, predicting pain progression, predicting treatment response, and performance of therapy and pain management. Results of these studies show promise for the improvement of patient outcomes. Current gaps in the research and subsequent directions for future study involve AI in optimizing and improving nerve stimulation and more thoroughly predicting patients' responses to treatment.

The Advances and Utility of Artificial Intelligence and Robotics in Regional Anesthesia: An Overview of Recent Developments

The integration of artificial intelligence (AI) and robotics in regional anesthesia has brought about transformative changes in acute pain management for surgical procedures. This review explores the evolving landscape of AI and robotics applications in regional anesthesia, outlining their potential benefits, challenges, and ethical considerations. AI-driven pain assessment, real-time guidance for needle placement during nerve blocks, and predictive modeling solutions for nerve blocks have the potential to enhance procedural precision and improve patient outcomes. Robotic technology aids in accurate needle insertion, reducing complications and improving pain relief. This review also highlights the ethical and safety considerations surrounding AI implementation, emphasizing data security and professional training. While challenges such as costs and regulatory hurdles exist, ongoing research and clinical trials demonstrate the practical utility of these technologies. In conclusion, AI and robotics have the potential to reshape regional anesthesia practice, ultimately improving patient care and procedural accuracy in pain management.