Effect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students: A Randomized Clinical Trial

Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis

PURPOSE

Data science approaches personalizing pediatric appendicitis management are hampered by small datasets and unstructured electronic medical records (EMR). Artificial intelligence (AI) chatbots based on large language models can structure free-text EMR data. We compare data extraction quality between ChatGPT-4 and human data collectors.

METHODS

To train AI models to grade pediatric appendicitis preoperatively, several data collectors extracted detailed preoperative and operative data from 2100 children operated for acute appendicitis. Collectors were trained for the task based on satisfactory Kappa scores. ChatGPT-4 was prompted to structure free text from 103 random anonymized ultrasound and operative records in the dataset using the set variables and coding options, and to estimate appendicitis severity grade from the operative report. A pediatric surgeon then adjudicated all data, identifying errors in each method.

RESULTS

Within the 44 ultrasound (42.7%) and 32 operative reports (31.1%) discordant in at least one field, 98% of the errors were found in the manual data extraction. The appendicitis grade was erroneously assigned manually in 29 patients (28.2%), and by ChatGPT-4 in 3 (2.9%). Across datasets, the use of the AI chatbot was able to avoid misclassification in 59.2% of the records including both reports and extracted data approximately 40 times faster.

CONCLUSION

AI chatbot significantly outperformed manual data extraction in accuracy for ultrasound and operative reports, and correctly assigned the appendicitis grade. While wider validation is required and data safety concerns must be addressed, these AI tools show significant promise in improving the accuracy and efficiency of research data collection.

LEVELS OF EVIDENCE

Level III.

Artificial intelligence in surgery

Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.

The Role of Artificial Intelligence in Medical Education: A Systematic Review

BACKGROUND

To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study.

METHODS

This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found.

RESULTS

After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%.

CONCLUSION

AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.

A scoping review of artificial intelligence in medical education: BEME Guide No. 84

BACKGROUND

Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research.

METHODS

This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape.

RESULTS

The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education.

CONCLUSION

The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.

ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions

BACKGROUND

Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis and reducing the workload of doctors. However, the absence of publicly available image datasets restricts the application of computer-assisted diagnostic techniques.

METHODS

In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with 7159 images in multiple formats totally. In order to prove the effectiveness of segmentation on ECPC-IDS, six deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with 3579 images and XML files with annotation information totally. Eight deep learning methods are selected for experiments on the detection task.

RESULTS

This study is conduct using deep learning-based semantic segmentation and object detection methods to demonstrate the distinguishability on ECPC-IDS. From a separate perspective, the minimum and maximum values of Dice on PET images are 0.546 and 0.743, respectively. The minimum and maximum values of Dice on CT images are 0.012 and 0.510, respectively. The target detection section's maximum mAP values on PET and CT images are 0.993 and 0.986, respectively.

CONCLUSION

As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multi-modality images. ECPC-IDS can assist researchers in exploring new algorithms to enhance computer-assisted diagnosis, benefiting both clinical doctors and patients. ECPC-IDS is also freely published for non-commercial at: https://figshare.com/articles/dataset/ECPC-IDS/23808258.

A comparative vignette study: Evaluating the potential role of a generative AI model in enhancing clinical decision-making in nursing

AIM

This study explores the potential of a generative artificial intelligence tool (ChatGPT) as clinical support for nurses. Specifically, we aim to assess whether ChatGPT can demonstrate clinical decision-making equivalent to that of expert nurses and novice nursing students. This will be evaluated by comparing ChatGPT responses to clinical scenarios to those of nurses on different levels of experience.

DESIGN

This is a cross-sectional study.

METHODS

Emergency room registered nurses (i.e. experts; n = 30) and nursing students (i.e. novices; n = 38) were recruited during March-April 2023. Clinical decision-making was measured using three validated clinical scenarios involving an initial assessment and reevaluation. Clinical decision-making aspects assessed were the accuracy of initial assessments, the appropriateness of recommended tests and resource use and the capacity to reevaluate decisions. Performance was also compared by timing response generations and word counts. Expert nurses and novice students completed online questionnaires (via Qualtrics), while ChatGPT responses were obtained from OpenAI.

RESULTS

Concerning aspects of clinical decision-making and compared to novices and experts: (1) ChatGPT exhibited indecisiveness in initial assessments; (2) ChatGPT tended to suggest unnecessary diagnostic tests; (3) When new information required re-evaluation, ChatGPT responses demonstrated inaccurate understanding and inappropriate modifications. In terms of performance, the mean number of words utilized in ChatGPT answers was 27-41 times greater than that utilized by both experts and novices; and responses were provided approximately 4 times faster than those of novices and twice faster than expert nurses. ChatGPT responses maintained logical structure and clarity.

CONCLUSIONS

A generative AI tool demonstrated indecisiveness and a tendency towards over-triage compared to human clinicians.

IMPACT

The study shows that it is important to approach the implementation of ChatGPT as a nurse's digital assistant with caution. More study is needed to optimize the model's training and algorithms to provide accurate healthcare support that aids clinical decision-making.

REPORTING METHOD

This study adhered to relevant EQUATOR guidelines for reporting observational studies.

PATIENT OR PUBLIC CONTRIBUTION

Patients were not directly involved in the conduct of this study.

Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems- A Four-Arm Randomized Control Trial

OBJECTIVE

To explore optimal feedback methodologies to enhance trainee skill acquisition in simulated surgical bimanual skills learning during brain tumor resections.

HYPOTHESES

(1) Providing feedback results in better learning outcomes in teaching surgical technical skill when compared to practice alone with no tailored performance feedback. (2) Providing more visual and visuospatial feedback results in better learning outcomes when compared to providing numerical feedback.

DESIGN

A prospective 4-parallel-arm randomized controlled trial.

SETTING

Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Canada.

PARTICIPANTS

Medical students (n = 120) from 4 Quebec medical schools.

RESULTS

Participants completed a virtually simulated tumor resection task 5 times while receiving 1 of 4 feedback based on their group allocation: (1) practice-alone without feedback, (2) numerical feedback, (3) visual feedback, and (4) visuospatial feedback. Outcome measures were participants' scores on 14-performance metrics and the number of expert benchmarks achieved during each task. There were no significant differences in the first task which determined baseline performance. A statistically significant interaction between feedback allocation and task repetition was found on the number of benchmarks achieved, F (10.558, 408.257)=3.220, p < 0.001. Participants in all feedback groups significantly improved their performance compared to baseline. The visual feedback group achieved significantly higher number of benchmarks than the practice-alone group by the third repetition of the task, p = 0.005, 95%CI [0.42 3.25]. Visual feedback and visuospatial feedback improved performance significantly by the second repetition of the task, p = 0.016, 95%CI [0.19 2.71] and p = 0.003, 95%CI [0.4 2.57], respectively.

CONCLUSION

Simulations with autonomous visual computer assistance may be effective pedagogical tools in teaching bimanual operative skills via visual and visuospatial feedback information delivery.

Development of Artificial Intelligence-Teaching Assistant System for Undergraduate Nursing Students: A Field-Testing Study

Keeping students engaged and motivated during online or class discussion may be challenging. Artificial intelligence has potential to facilitate active learning by enhancing student engagement, motivation, and learning outcomes. The purpose of this study was to develop, test usability of, and explore undergraduate nursing students' perceptions toward the Artificial Intelligence-Teaching Assistant System. The system was developed based on three main components: machine tutor intelligence, a graphical user interface, and a communication connector. They were included in the system to support contextual machine tutoring. A field-testing study design, a mixed-method approach, was utilized with questionnaires and focus group interview. Twenty-one undergraduate nursing students participated in this study, and they interacted with the system for 2 hours following the required activity checklist. The students completed the validated usability questionnaires and then participated in the focus group interview. Descriptive statistics were used to analyze quantitative data, and thematic analysis was used to analyze qualitative data from the focus group interviews. The results showed that the Artificial Intelligence-Teaching Assistant System was user-friendly. Four main themes emerged, namely, functionality, feasibility, artificial unintelligence, and suggested learning modality. However, Artificial Intelligence-Teaching Assistant System functions, user interface, and content can be improved before full implementation.

Continuous Instrument Tracking in a Cerebral Corticectomy Ex Vivo Calf Brain Simulation Model: Face and Content Validation

BACKGROUND AND OBJECTIVES

Subpial corticectomy involving complete lesion resection while preserving pial membranes and avoiding injury to adjacent normal tissues is an essential bimanual task necessary for neurosurgical trainees to master. We sought to develop an ex vivo calf brain corticectomy simulation model with continuous assessment of surgical instrument movement during the simulation. A case series study of skilled participants was performed to assess face and content validity to gain insights into the utility of this training platform, along with determining if skilled and less skilled participants had statistical differences in validity assessment.

METHODS

An ex vivo calf brain simulation model was developed in which trainees performed a subpial corticectomy of three defined areas. A case series study assessed face and content validity of the model using 7-point Likert scale questionnaires.

RESULTS

Twelve skilled and 11 less skilled participants were included in this investigation. Overall median scores of 6.0 (range 4.0-6.0) for face validity and 6.0 (range 3.5-7.0) for content validity were determined on the 7-point Likert scale, with no statistical differences between skilled and less skilled groups identified.

CONCLUSION

A novel ex vivo calf brain simulator was developed to replicate the subpial resection procedure and demonstrated face and content validity.

Validity evidence supporting clinical skills assessment by artificial intelligence compared with trained clinician raters

BACKGROUND

Artificial intelligence (AI) is becoming increasingly used in medical education, but our understanding of the validity of AI-based assessments (AIBA) as compared with traditional clinical expert-based assessments (EBA) is limited. In this study, the authors aimed to compare and contrast the validity evidence for the assessment of a complex clinical skill based on scores generated from an AI and trained clinical experts, respectively.

METHODS

The study was conducted between September 2020 to October 2022. The authors used Kane's validity framework to prioritise and organise their evidence according to the four inferences: scoring, generalisation, extrapolation and implications. The context of the study was chorionic villus sampling performed within the simulated setting. AIBA and EBA were used to evaluate performances of experts, intermediates and novice based on video recordings. The clinical experts used a scoring instrument developed in a previous international consensus study. The AI used convolutional neural networks for capturing features on video recordings, motion tracking and eye movements to arrive at a final composite score.

RESULTS

A total of 45 individuals participated in the study (22 novices, 12 intermediates and 11 experts). The authors demonstrated validity evidence for scoring, generalisation, extrapolation and implications for both EBA and AIBA. The plausibility of assumptions related to scoring, evidence of reproducibility and relation to different training levels was examined. Issues relating to construct underrepresentation, lack of explainability, and threats to robustness were identified as potential weak links in the AIBA validity argument compared with the EBA validity argument.

CONCLUSION

There were weak links in the use of AIBA compared with EBA, mainly in their representation of the underlying construct but also regarding their explainability and ability to transfer to other datasets. However, combining AI and clinical expert-based assessments may offer complementary benefits, which is a promising subject for future research.

Unveiling the potential of artificial intelligence in orthopaedic surgery

Artificial intelligence is paving the way in contemporary medical advances, with the potential to revolutionise orthopaedic surgical care. By harnessing the power of complex algorithms, artificial intelligence yields outputs that have diverse applications including, but not limited to, identifying implants, diagnostic imaging for fracture and tumour recognition, prognostic tools through the use of electronic medical records, assessing arthroplasty outcomes, length of hospital stay and economic costs, monitoring the progress of functional rehabilitation, and innovative surgical training via simulation. However, amid the promising potential and enthusiasm surrounding artificial intelligence, clinicians should understand its limitations, and caution is needed before artificial intelligence-driven tools are introduced to clinical practice.

Can gamified surgical sets improve surgical instrument recognition and student performance retention in the operating room? A multi-institutional experimental crossover study

INTRODUCTION

Surgery requires a high degree of precision, speed, and concentration. Owing to the complexity of the modern world, traditional methods cannot meet these requirements. Therefore, in this study, we investigated students' diagnostic skills in the Operating Room in the context of surgical instruments by using gamification of surgical instruments and a crossover design.

METHOD

The study design was a multi-institutional quasi-experimental crossover and involved a three-arm intervention (with gender-specific block randomisation: Group A, B, and C) with a pre-test and three post-tests. A total of 90 students fell into three groups of 30 participants each. The surgical sets were learned for one semester through game-based instruction and traditional teaching, and then three OSCE tests were administered with time and location differences. Using one-way ANOVA, OSCE results were compared in the game, traditional, and control groups. The effectiveness of the intervention was tested in each group by repeated measures.

RESULT

The pretest scores of all three groups did not differ significantly. In the OSCE tests, both groups, A and B, performed similarly. However, these tests showed a significant difference in grouping between training through games and training in the traditional way. There was no significant difference between OSCE tests 2 and 3 in the game-based training group, indicating that what was learned was retained, while in the traditional method training group, OSCE 3 test scores declined significantly. Furthermore, repeated measures showed the effectiveness of game-based training.

CONCLUSION

In this study, gamification has turned out to be very effective in helping learners learn practical skills and leading to more sustainable learning.

Performance in a Simulated Virtual Reality Anterior Cervical Discectomy and Fusion Task: Disc Residual, Rate of Removal, and Efficiency Analyses

BACKGROUND AND OBJECTIVES

Anterior cervical discectomy and fusion (ACDF) is among the most common spine procedures. The Sim-Ortho virtual reality simulator platform contains a validated ACDF simulated task for performance assessment. This study aims to develop a methodology to extract three-dimensional data and reconstruct and quantitate specific simulated disc tissues to generate novel metrics to analyze performance metrics of skilled and less skilled participants.

METHODS

We used open-source platforms to develop a methodology to extract three-dimensional information from ACDF simulation data. Metrics generated included, efficiency index, disc volumes removed from defined regions, and rate of tissue removal from superficial, central, and deep disc regions. A pilot study was performed to assess the utility of this methodology to assess expertise during the ACDF simulated procedure.

RESULTS

The system outlined, extracts data allowing the development of a methodology which accurately reconstructs and quantitates 3-dimensional disc volumes. In the pilot study, data sets from 27 participants, divided into postresident, resident, and medical student groups, allowed assessment of multiple novel metrics, including efficiency index (surgical time spent in actively removing disc), where the postresident group spent 61.8% of their time compared with 53% and 30.2% for the resident and medical student groups, respectively ( P = .01). During the annulotomy component, the postresident group removed 47.4% more disc than the resident groups and 102% more than the medical student groups ( P = .03).

CONCLUSION

The methodology developed in this study generates novel surgical procedural metrics from 3-dimensional data generated by virtual reality simulators and can be used to assess surgical performance.

Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature

The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.

AI in Surgical Curriculum Design and Unintended Outcomes for Technical Competencies in Simulation Training

Importance

To better elucidate the role of artificial intelligence (AI) in surgical skills training requires investigations in the potential existence of a hidden curriculum.

Objective

To assess the pedagogical value of AI-selected technical competencies and their extended effects in surgical simulation training.

Design, Setting, and Participants

This cohort study was a follow-up of a randomized clinical trial conducted at the Neurosurgical Simulation and Artificial Intelligence Learning Centre at the Montreal Neurological Institute, McGill University, Montreal, Canada. Surgical performance metrics of medical students exposed to an AI-enhanced training curriculum were compared with a control group of participants who received no feedback and with expert benchmarks. Cross-sectional data were collected from January to April 2021 from medical students and from March 2015 to May 2016 from experts. This follow-up secondary analysis was conducted from June to September 2022. Participants included medical students (undergraduate year 0-2) in the intervention cohorts and neurosurgeons to establish expertise benchmarks.

Exposure

Performance assessment and personalized feedback by an intelligent tutor on 4 AI-selected learning objectives during simulation training.

Main Outcomes and Measures

Outcomes of interest were unintended performance outcomes, measured by significant within-participant difference from baseline in 270 performance metrics in the intervention cohort that was not observed in the control cohort.

Results

A total of 46 medical students (median [range] age, 22 [18-27] years; 27 [59%] women) and 14 surgeons (median [range] age, 45 [35-59] years; 14 [100%] men) were included in this study, and no participant was lost to follow-up. Feedback on 4 AI-selected technical competencies was associated with additional performance change in 32 metrics over the entire procedure and 20 metrics during tumor removal that was not observed in the control group. Participants exposed to the AI-enhanced curriculum demonstrated significant improvement in safety metrics, such as reducing the rate of healthy tissue removal (mean difference, -7.05 × 10-5 [95% CI, -1.09 × 10-4 to -3.14 × 10-5] mm3 per 20 ms; P < .001) and maintaining a focused bimanual control of the operative field (mean difference in maximum instrument divergence, -4.99 [95% CI, -8.48 to -1.49] mm, P = .006) compared with the control group. However, negative unintended effects were also observed. These included a significantly lower velocity and acceleration in the dominant hand (velocity: mean difference, -0.13 [95% CI, -0.17 to -0.09] mm per 20 ms; P < .001; acceleration: mean difference, -2.25 × 10-2 [95% CI, -3.20 × 10-2 to -1.31 × 10-2] mm per 20 ms2; P < .001) and a significant reduction in the rate of tumor removal (mean difference, -4.85 × 10-5 [95% CI, -7.22 × 10-5 to -2.48 × 10-5] mm3 per 20 ms; P < .001) compared with control. These unintended outcomes diverged students' movement and efficiency performance metrics away from the expertise benchmarks.

Conclusions and Relevance

In this cohort study of medical students, an AI-enhanced curriculum for bimanual surgical skills resulted in unintended changes that improved performance in safety but negatively affected some efficiency metrics. Incorporating AI in course design requires ongoing assessment to maintain transparency and foster evidence-based learning objectives.

Neurosurgical skills analysis by machine learning models: systematic review

Machine learning (ML) models are being actively used in modern medicine, including neurosurgery. This study aimed to summarize the current applications of ML in the analysis and assessment of neurosurgical skills. We conducted this systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched the PubMed and Google Scholar databases for eligible studies published until November 15, 2022, and used the Medical Education Research Study Quality Instrument (MERSQI) to assess the quality of the included articles. Of the 261 studies identified, we included 17 in the final analysis. Studies were most commonly related to oncological, spinal, and vascular neurosurgery using microsurgical and endoscopic techniques. Machine learning-evaluated tasks included subpial brain tumor resection, anterior cervical discectomy and fusion, hemostasis of the lacerated internal carotid artery, brain vessel dissection and suturing, glove microsuturing, lumbar hemilaminectomy, and bone drilling. The data sources included files extracted from VR simulators and microscopic and endoscopic videos. The ML application was aimed at classifying participants into several expertise levels, analysis of differences between experts and novices, surgical instrument recognition, division of operation into phases, and prediction of blood loss. In two articles, ML models were compared with those of human experts. The machines outperformed humans in all tasks. The most popular algorithms used to classify surgeons by skill level were the support vector machine and k-nearest neighbors, and their accuracy exceeded 90%. The "you only look once" detector and RetinaNet usually solved the problem of detecting surgical instruments - their accuracy was approximately 70%. The experts differed by more confident contact with tissues, higher bimanuality, smaller distance between the instrument tips, and relaxed and focused state of the mind. The average MERSQI score was 13.9 (from 18). There is growing interest in the use of ML in neurosurgical training. Most studies have focused on the evaluation of microsurgical skills in oncological neurosurgery and on the use of virtual simulators; however, other subspecialties, skills, and simulators are being investigated. Machine learning models effectively solve different neurosurgical tasks related to skill classification, object detection, and outcome prediction. Properly trained ML models outperform human efficacy. Further research on ML application in neurosurgery is needed.

Predictive Analytics and Artificial Intelligence in Surgery-Opportunities and Risks

This Viewpoint discusses the opportunities and risks of using 3 main areas of artificial intelligence in surgery: computer vision, digital transformation at the point of care, and electronic health records data.

Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning

Finite element methods (FEM) are popular approaches for simulation of soft tissues with elastic or viscoelastic behavior. However, their usage in real-time applications, such as in virtual reality surgical training, is limited by computational cost. In this application scenario, which typically involves transportable simulators, the computing hardware severely constrains the size or the level of details of the simulated scene. To address this limitation, data-driven approaches have been suggested to simulate mechanical deformations by learning the mapping rules from FEM generated datasets. Prior data-driven approaches have ignored the physical laws of the underlying engineering problem and have consequently been restricted to simulation cases of simple hyperelastic materials where the temporal variations were effectively ignored. However, most surgical training scenarios require more complex hyperelastic models to deal with the viscoelastic properties of tissues. This type of material exhibits both viscous and elastic behaviors when subjected to external force, requiring the implementation of time-dependant state variables. Herein, we propose a deep learning method for predicting displacement fields of soft tissues with viscoelastic properties. The main contribution of this work is the use of a physics-guided loss function for the optimization of the deep learning model parameters. The proposed deep learning model is based on convolutional (CNN) and recurrent layers (LSTM) to predict spatiotemporal variations. It is augmented with a mass conservation law in the lost function to prevent the generation of physically inconsistent results. The deep learning model is trained on a set of FEM datasets that are generated from a commercially available state-of-the-art numerical neurosurgery simulator. The use of the physics-guided loss function in a deep learning model has led to a better generalization in the prediction of deformations in unseen simulation cases. Moreover, the proposed method achieves a better accuracy over the conventional CNN models, where improvements were observed in unseen tissue from 8% to 30% depending on the magnitude of external forces. It is hoped that the present investigation will help in filling the gap in applying deep learning in virtual reality simulators, hence improving their computational performance (compared to FEM simulations) and ultimately their usefulness.

Artificial Intelligence in Surgical Learning

(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.

Advancing Surgical Education: The Use of Artificial Intelligence in Surgical Training

The technology of artificial intelligence (AI) has made significant in-roads into the field of medicine over the last decade. With surgery being a discipline where repetition is the key to mastery, the scope of AI presents enormous potential for resident education through the analysis of technique and delivery of structured feedback for performance improvement. In an era marred by a raging pandemic that has decreased exposure and opportunity, AI offers an attractive solution towards improving operating room efficiency, safe patient care in the hands of supervised residents and can ultimately culminate in reduced health care costs. Through this article, we elucidate the current adoption of the artificial intelligence technology and its prospects for advancing surgical education.

Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study

BACKGROUND

Anthropomorphic phantoms are used in surgical planning and intervention. Ideal accuracy and high efficiency are prerequisites for its clinical application. We aimed to develop a fully automated artificial intelligence-based three-dimensional (3D) reconstruction system (AI system) to assist thoracic surgery and to determine its accuracy, efficiency, and safety for clinical use.

METHODS

This AI system was developed based on a 3D convolutional neural network (CNN) and optimized by gradient descent after training with 500 cases, achieving a Dice coefficient of 89.2%. Accuracy was verified by comparing virtual structures predicted by the AI system with anatomical structures of patients in retrospective (n = 113) and prospective cohorts (n = 139) who underwent lobectomy or segmentectomy at the Peking University Cancer Hospital. Operation time and blood loss were compared between the retrospective cohort (without AI assistance) and prospective cohort (with AI assistance) for safety evaluation. The time consumption for reconstruction and the quality score were compared between the AI system and manual reconstruction software (Mimics®) for efficiency validation. This study was registered at https://www.chictr.org.cn as ChiCTR2100050985.

FINDINGS

The AI system reconstructed 13,608 pulmonary segmental branches from retrospective and prospective cohorts, and 1573 branches of interest corresponding to phantoms were detectable during the operation for verification, achieving 100% and 97% accuracy for segmental bronchi, 97.2% and 99.1% for segmental arteries, and 93.2% and 98.8% for segmental veins, respectively. With the assistance of the AI system, the operation time was shortened by 24.5 min for lobectomy (p < 0.001) and 20 min for segmentectomy (p = 0.007). Compared to Mimics®, the AI system reduced the model reconstruction time by 14.2 min (p < 0.001), and it also outperformed Mimics® in model quality scores (p < 0.001).

INTERPRETATION

The AI system can accurately predict thoracic anatomical structures with higher efficiency than manual reconstruction software. Constant optimization and larger population validation are required.

FUNDING

This study was funded by the Beijing Natural Science Foundation (No. L222020) and other sources.

The role of artificial intelligence in surgical simulation

Artificial Intelligence (AI) plays an integral role in enhancing the quality of surgical simulation, which is increasingly becoming a popular tool for enriching the training experience of a surgeon. This spans the spectrum from facilitating preoperative planning, to intraoperative visualisation and guidance, ultimately with the aim of improving patient safety. Although arguably still in its early stages of widespread clinical application, AI technology enables personal evaluation and provides personalised feedback in surgical training simulations. Several forms of surgical visualisation technologies currently in use for anatomical education and presurgical assessment rely on different AI algorithms. However, while it is promising to see clinical examples and technological reports attesting to the efficacy of AI-supported surgical simulators, barriers to wide-spread commercialisation of such devices and software remain complex and multifactorial. High implementation and production costs, scarcity of reports evidencing the superiority of such technology, and intrinsic technological limitations remain at the forefront. As AI technology is key to driving the future of surgical simulation, this paper will review the literature delineating its current state, challenges, and prospects. In addition, a consolidated list of FDA/CE approved AI-powered medical devices for surgical simulation is presented, in order to shed light on the existing gap between academic achievements and the universal commercialisation of AI-enabled simulators. We call for further clinical assessment of AI-supported surgical simulators to support novel regulatory body approved devices and usher surgery into a new era of surgical education.

The impact of tutor expertise on the students' scores in active learning methods: a meta-analysis

INTRODUCTION

The tutor's role has been changing in the active learning methods in comparison to the traditional ones. Tutors are encouraged to become facilitators and to guide the students to construct a new knowledge by making bridges with the old one. Tutor's expertise in the active methods has been discussed with different results. The aim of this study was to assess the impact of tutor expertise on the students' scores in active learning methods.

METHODS

The authors conducted this meta-analysis under the guidelines of a critical tool for systematic reviews (AMSTAR2). PubMed, Embase, Cochrane library, Scopus and Science Direct were the databases used for searching. The articles included compared students' learning scores according to tutor expertise between 2 groups of students with an active method of learning. The Review Manager software 5.3 was used to conduct this meta-analysis. We considered the mean difference as the effect size.

RESULTS

3169 students and 222 learning groups of 5 to 10 students were included in this study. The combined mean difference reached 0.84 with 95% CI [0.22, 1.46]. A significant difference between the two groups was observed in favour of the non-expert group (p=0.008). The heterogeneity I-square was evaluated to 98%. The Funnel plot reflected no publication bias (p=0.21). A sub-group analysis was performed taking into account the studies dealing with medical curriculum and the assessment methods used. It showed a significant difference between the two groups in favour of non-expert tutors (p<0.05).

CONCLUSION

Even if the results of this meta-analysis couldn't be conclusive and can't induce recommendations, they highlighted the tendency of non-expert tutors to be more student-centred. The heterogeneity observed can be decreased by establishing consensual definitions of expertise and assessment tools in further research studies in order to reflect the validity and efficiency of different tutoring styles in active learning methods.

CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy

Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the training data can improve state-of-the-art polyp detection/segmentation methods as well as drive next generation of OC simulators for training medical students. The code and pre-trained models for CLTS-GAN are available on Computational Endoscopy Platform GitHub (https://github.com/nadeemlab/CEP).

The Application of Rehabilitation Therapy Occupational Competency Evaluation Model in the Improvement of College Students’ Innovation and Entrepreneurship

This paper constructs an evaluation model of occupational competency in rehabilitation therapy and applies it to the improvement of college students’ innovative and entrepreneurial competencies. Based on clarifying the connotation of college students’ entrepreneurial competency, this study carries out research on the evaluation system of college students’ entrepreneurial competency. First, the method of qualitative research is used to conduct in-depth interviews with college students and entrepreneurial mentors to understand the views of these groups on college students’ entrepreneurial competency, and the coding of the interview contents is carried out by applying the rooting theory, and finally, a theoretical model of the composition of college students’ entrepreneurial competency is derived. The front-line position of talent cultivation is the construction of faculty, because the faculty with high-quality vocational ability is an indispensable prerequisite for the development of universities, and the teaching level of the faculty is the key factor for the effectiveness of teaching to appear. Based on the perspective of role theory, we analyze the problems and reasons for the lack of competency in the role-playing process; integrate the three stages of understanding the role, playing the role, and adjusting the role with the competency model; and propose strategies to improve the competency of youth social workers in the process of playing the role, to help workers accurately understand the role, correctly play the role, and reasonably adjust the role. The curriculum is designed to cultivate the competency of college student nursing positions, which is conducive to improving the professional quality of college student nursing service providers and regulating college student nursing practice so that they can actively respond to problems. This will strengthen the entire teaching faculty and enhance the professional competence of full-time teachers.

Continuous monitoring of surgical bimanual expertise using deep neural networks in virtual reality simulation

In procedural-based medicine, the technical ability can be a critical determinant of patient outcomes. Psychomotor performance occurs in real-time, hence a continuous assessment is necessary to provide action-oriented feedback and error avoidance guidance. We outline a deep learning application, the Intelligent Continuous Expertise Monitoring System (ICEMS), to assess surgical bimanual performance at 0.2-s intervals. A long-short term memory network was built using neurosurgeon and student performance in 156 virtually simulated tumor resection tasks. Algorithm predictive ability was tested separately on 144 procedures by scoring the performance of neurosurgical trainees who are at different training stages. The ICEMS successfully differentiated between neurosurgeons, senior trainees, junior trainees, and students. Trainee average performance score correlated with the year of training in neurosurgery. Furthermore, coaching and risk assessment for critical metrics were demonstrated. This work presents a comprehensive technical skill monitoring system with predictive validation throughout surgical residency training, with the ability to detect errors.