Artificial intelligence in laparoscopic simulation: a promising future for large-scale automated evaluations

Artificial intelligence facilitates the potential of simulator training: An innovative laparoscopic surgical skill validation system using artificial intelligence technology

PURPOSE

The development of innovative solutions, such as simulator training and artificial intelligence (AI)-powered tutoring systems, has significantly changed surgical trainees' environments to receive the intraoperative instruction necessary for skill acquisition. In this study, we developed a new objective assessment system using AI for forceps manipulation in a surgical training simulator.

METHODS

Laparoscopic exercises were recorded using an iPad®, which provided top and side views. Top-view movies were used for AI learning of forceps trajectory. Side-view movies were used as supplementary information to assess the situation. We used an AI-based posture estimation method, DeepLabCut (DLC), to recognize and positionally measure the forceps in the operating field. Tracking accuracy was quantitatively evaluated by calculating the pixel differences between the annotation points and the points predicted by the AI model. Tracking stability at specified key points was verified to assess the AI model.

RESULTS

We selected a random sample to evaluate tracking accuracy quantitatively. This sample comprised 5% of the frames not used for AI training from the complete set of video frames. We compared the AI detection positions and correct positions and found an average pixel discrepancy of 9.2. The qualitative evaluation of the tracking stability was good at the forceps hinge; however, forceps tip tracking was unstable during rotation.

CONCLUSION

The AI-based forceps tracking system can visualize and evaluate laparoscopic surgical skills. Improvements in the proposed system and AI self-learning are expected to enable it to distinguish the techniques of expert and novice surgeons accurately. This system is a useful tool for surgeon training and assessment.

Advances in gynecologic simulation: implementation, validity, and new resources

PURPOSE OF REVIEW

Simulation plays an integral role in gynecological surgical training that will continue and grow in the future. It aims to supplement surgical training as residents are experiencing decreased surgical volumes. It has also been recognized as a means for assessing a trainee's capabilities to the point of being incorporated into the board certification process. This review will highlight the recent updates in research and resources available for gynecologic surgery simulation.

RECENT FINDINGS

The literature reviewed demonstrated that, across the board, participants in simulation find it beneficial. A variety of low-fidelity and high-fidelity models, virtual reality programs, and artificial intelligence technology have been developed recently. As the field advances, researchers have started to investigate methods to increased engagement and provide feedback without the need for direct observation, the logical next step considerations to maximize the impact and availability of simulation.

SUMMARY

Gynecologic surgery training programs must stay abreast of the wide variety of simulation training available. Efforts should continue to be made to investigate the impact of simulation patient outcomes. The academic medicine community has a responsibility to ensure high-quality simulation is available to all surgical trainees to avoid disparities between low-resource and high-resource programs.

Validation of a novel, low-fidelity virtual reality simulator and an artificial intelligence assessment approach for peg transfer laparoscopic training

Simulators are widely used in medical education, but objective and automatic assessment is not feasible with low-fidelity simulators, which can be solved with artificial intelligence (AI) and virtual reality (VR) solutions. The effectiveness of a custom-made VR simulator and an AI-based evaluator of a laparoscopic peg transfer exercise was investigated. Sixty medical students were involved in a single-blinded randomised controlled study to compare the VR simulator with the traditional box trainer. A total of 240 peg transfer exercises from the Fundamentals of Laparoscopic Surgery programme were analysed. The experts and AI-based software used the same criteria for evaluation. The algorithm detected pitfalls and measured exercise duration. Skill improvement showed no significant difference between the VR and control groups. The AI-based evaluator exhibited 95% agreement with the manual assessment. The average difference between the exercise durations measured by the two evaluation methods was 2.61 s. The duration of the algorithmic assessment was 59.47 s faster than the manual assessment. The VR simulator was an effective alternative practice compared with the training box simulator. The AI-based evaluation produced similar results compared with the manual assessment, and it could significantly reduce the evaluation time. AI and VR could improve the effectiveness of basic laparoscopic training.

Surgeons vs ChatGPT: Assessment and Feedback Performance Based on Real Surgical Scenarios

INTRODUCTION

Artificial intelligence tools are being progressively integrated into medicine and surgical education. Large language models, such as ChatGPT, could provide relevant feedback aimed at improving surgical skills. The purpose of this study is to assess ChatGPT´s ability to provide feedback based on surgical scenarios.

METHODS

Surgical situations were transformed into texts using a neutral narrative. Texts were evaluated by ChatGPT 4.0 and 3 surgeons (A, B, C) after a brief instruction was delivered: identify errors and provide feedback accordingly. Surgical residents were provided with each of the situations and feedback obtained during the first stage, as written by each surgeon and ChatGPT, and were asked to assess the utility of feedback (FCUR) and its quality (FQ). As control measurement, an Education-Expert (EE) and a Clinical-Expert (CE) were asked to assess FCUR and FQ.

RESULTS

Regarding residents' evaluations, 96.43% of times, outputs provided by ChatGPT were considered useful, comparable to what surgeons' B and C obtained. Assessing FQ, ChatGPT and all surgeons received similar scores. Regarding EE's assessment, ChatGPT obtained a significantly higher FQ score when compared to surgeons A and B (p = 0.019; p = 0.033) with a median score of 8 vs. 7 and 7.5, respectively; and no difference respect surgeon C (score of 8; p = 0.2). Regarding CE´s assessment, surgeon B obtained the highest FQ score while ChatGPT received scores comparable to that of surgeons A and C. When participants were asked to identify the source of the feedback, residents, CE, and EE perceived ChatGPT's outputs as human-provided in 33.9%, 28.5%, and 14.3% of cases, respectively.

CONCLUSION

When given brief written surgical situations, ChatGPT was able to identify errors with a detection rate comparable to that of experienced surgeons and to generate feedback that was considered useful for skill improvement in a surgical context performing as well as surgical instructors across assessments made by general surgery residents, an experienced surgeon, and a nonsurgeon feedback expert.

Future directions for simulation in healthcare: A critical review

There was a widespread discontinuation of simulation programs during and after the COVID-19 pandemic. The objective is to explore how to facilitate greater integration of simulation in healthcare organizations. A literature review was conducted in PubMed, MEDES, IBECS and DOCUMED databases. Twenty-three articles published after the pandemic were selected, categorized in seven themes and critically reviewed. In order to consistently and fully integrate simulation into the organizational culture it is recommended to prioritize the development of new strategies that enhance the efficiency and safety of healthcare delivery. And also strategies that enhance the satisfaction and well-being of all stakeholders.

Using Artificial Intelligence to Gauge Competency on a Novel Laparoscopic Training System

OBJECTIVE

Laparoscopic surgical skill assessment and machine learning are often inaccessible to low-and-middle-income countries (LMIC). Our team developed a low-cost laparoscopic training system to teach and assess psychomotor skills required in laparoscopic salpingostomy in LMICs. We performed video review using AI to assess global surgical techniques. The objective of this study was to assess the validity of artificial intelligence (AI) generated scoring measures of laparoscopic simulation videos by comparing the accuracy of AI results to human-generated scores.

DESIGN

Seventy-four surgical simulation videos were collected and graded by human participants using a modified OSATS (Objective Structured Assessment of Technical Skills). The videos were then analyzed via AI using 3 different time and distance-based calculations of the laparoscopic instruments including path length, dimensionless jerk, and standard deviation of tool position. Predicted scores were generated using 5-fold cross validation and K-Nearest-Neighbors to train classifiers.

SETTING

Surgical novices and experts from a variety of hospitals in Ethiopia, Cameroon, Kenya, and the United States contributed 74 laparoscopic salpingostomy simulation videos.

RESULTS

Complete accuracy of AI compared to human assessment ranged from 65-77%. There were no statistical differences in rank mean scores for 3 domains, Flow of Operation, Respect for Tissue, and Economy of Motion, while there were significant differences in ratings for Instrument Handling, Overall Performance, and the total summed score of all 5 domains (Summed). Estimated effect sizes were all less than 0.11, indicating very small practical effect. Estimated intraclass correlation coefficient (ICC) of Summed was 0.72 indicating moderate correlation between AI and Human scores.

CONCLUSIONS

Video review using AI technology of global characteristics was similar to that of human review in our laparoscopic training system. Machine learning may help fill an educational gap in LMICs where direct apprenticeship may not be feasible.

Current clinical applications of artificial intelligence in shoulder surgery: what the busy shoulder surgeon needs to know and what's coming next

Background

Artificial intelligence (AI) is a continuously expanding field with the potential to transform a variety of industries-including health care-by providing automation, efficiency, precision, accuracy, and decision-making support for simple and complex tasks. Basic knowledge of the key features as well as limitations of AI is paramount to understand current developments in this field and to successfully apply them to shoulder surgery. The purpose of the present review is to provide an overview of AI within orthopedics and shoulder surgery exploring current and forthcoming AI applications.

Methods

PubMed and Scopus databases were searched to provide a narrative review of the most relevant literature on AI applications in shoulder surgery.

Results

Despite the enormous clinical and research potential of AI, orthopedic surgery has been a relatively late adopter of AI technologies. Image evaluation, surgical planning, aiding decision-making, and facilitating patient evaluations over time are some of the current areas of development with enormous opportunities to improve surgical practice, research, and education. Furthermore, the advancement of AI-driven strategies has the potential to create a more efficient medical system that may reduce the overall cost of delivering and implementing quality health care for patients with shoulder pathology.

Conclusion

AI is an expanding field with the potential for broad clinical and research applications in orthopedic surgery. Many challenges still need to be addressed to fully leverage the potential of AI to clinical practice and research such as privacy issues, data ownership, and external validation of the proposed models.

Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM)

The landscape of surgical training is rapidly evolving with the advent of artificial intelligence (AI) and its integration into education and simulation. This manuscript aims to explore the potential applications and benefits of AI-assisted surgical training, particularly the use of large language models (LLMs), in enhancing communication, personalizing feedback, and promoting skill development. We discuss the advancements in simulation-based training, AI-driven assessment tools, video-based assessment systems, virtual reality (VR) and augmented reality (AR) platforms, and the potential role of LLMs in the transcription, translation, and summarization of feedback. Despite the promising opportunities presented by AI integration, several challenges must be addressed, including accuracy and reliability, ethical and privacy concerns, bias in AI models, integration with existing training systems, and training and adoption of AI-assisted tools. By proactively addressing these challenges and harnessing the potential of AI, the future of surgical training may be reshaped to provide a more comprehensive, safe, and effective learning experience for trainees, ultimately leading to better patient outcomes. .

The silver lining of the pandemic in surgical education: virtual surgical education and recommendations for best practices

Virtual education is an evolving field within the realm of surgical training. Since the onset of the COVID-19 pandemic, the application of virtual technologies in surgical education has undergone significant exploration and advancement. While originally developed to supplement in-person curricula for the development of clinical decision-making, virtual surgical education has expanded into the realms of clinical decision-making, surgical, and non-surgical skills acquisition. This manuscript aims to discuss the various applications of virtual surgical education as well as the advantages and disadvantages associated with each education modality, while offering recommendations on best practices and future directions.