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.

The PLET (Portable Laparoscopic Endo-Trainer) study: a randomized controlled trial of home- versus hospital-based surgical training

PURPOSE

The purpose of this study was to assess the effect of training with a personal, portable laparoscopic endo-trainer (PLET) on residents' laparoscopic skills.

METHODS

The study took place at a tertiary-care academic university hospital in Switzerland. All participants were randomized to either a home- or hospital-based PLET training group, and surgical skill performance was assessed using five laparoscopic exercises. 24 surgical residents, 13 females and 11 males, were enrolled at any training stage. Nine residents completed the assessments. Endpoints consisted of subjective and objective assessment ratings as well as exercise time and qualitative data up to 12 weeks. The primary outcome was the difference in exercise time and secondary outcomes included performance scores as well as qualitative data.

RESULTS

The hospital-based training group performed exercises number 1, 3 and 4 faster at 12 weeks than at baseline (p = .003, < 0.001 and 0.024). Surgical skill performance was not statistically significantly different in any of the endpoints between the hospital- and home-based training groups at 12 weeks. Both the subjective and objective assessment ratings significantly improved in the hospital-based training group between baseline and 12 weeks (p = .006 and 0.003, respectively). There was no statistically significant improvement in exercise time as well as subjective and objective assessment ratings over time in the home-based training group. The qualitative data suggested that participants who were randomized to the hospital-based training group wished to have the PLET at home and vice versa. Several participants across groups lacked motivation because of their workload or time constraints, though most believed the COVID-19 pandemic had no influence on their motivation or the time they had for training.

CONCLUSION

The PLET enhances laparoscopic surgical skills over time in a hospital-based training setting. In order to understand and optimize motivational factors, further research is needed.

TRIAL REGISTRATION

This trial was retrospectively registered on clinicaltrials.gov (NCT06301230).

Dental student application of artificial intelligence technology in detecting proximal caries lesions

OBJECTIVES

This study aimed to investigate the caries diagnosis performances of dental students after training with an artificial intelligence (AI) application utilizing deep learning techniques, a type of artificial neural network.

METHODS

A total of 1200 bitewing radiographs were obtained from the institution's database and two specialist dentists labeled the caries lesions in the images. Randomly selected 1000 images were used for training purposes and the remaining 200 radiographs were used to evaluate the caries diagnostic performance of the AI. Then, a convolutional neural network, a deep learning algorithm commonly employed to analyze visual imagery problems, called "You Only Look Once," was modified and trained to detect enamel and dentin caries lesions in the radiographs. Forty dental students were selected voluntarily and randomly divided into two groups. The pre-test results of dental caries diagnosis performances of both groups were recorded. After 1 week, group 2 students were trained using an AI application. Then, the post-test results of both groups were recorded. The labeling duration of the students was also measured and analyzed.

RESULTS

When both groups' pre-test and post-test results were evaluated, a statistically significant improvement was found for all parameters examined except precision score (p < 0.05). However, the trained group's accuracy, sensitivity, specificity, and F1 scores were significantly higher than the non-trained group in terms of post-test scores (p < 0.05). In group 2 (trained group), the post-test labeling time was considerably increased (p < 0.05).

CONCLUSIONS

The students trained by AI showed promising results in detecting caries lesions. The use of AI can also contribute to the clinical education of dental students.

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.

Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review

BACKGROUND

Technical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technical skill assessment. The aim of this systematic review was to analyze the performance, external validity, and generalizability of AI models for technical skill assessment in minimally invasive surgery.

METHODS

A systematic search of Medline, Embase, Web of Science, and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of the included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.

RESULTS

In total, 1958 articles were identified, 50 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (n = 25) or kinematic data from robotic systems or sensors (n = 22) were the most frequent input data for AI. Most studies used deep learning (n = 34) and predicted technical skills using an ordinal assessment scale (n = 36) with good accuracies in simulated settings. However, all proposed models were in development stage, only 4 studies were externally validated and 8 showed a low RoB.

CONCLUSION

AI showed good performance in technical skill assessment in minimally invasive surgery. However, models often lacked external validity and generalizability. Therefore, models should be benchmarked using predefined performance metrics and tested in clinical implementation studies.

Considerations for the application of artificial intelligence in vascular surgical education

The rapid adoption of artificial intelligence (AI) into everyday use has presented multiple issues for surgical educators to consider. In this article, the authors discuss some of the ethical aspects of academic integrity and the use of AI. These issues include the importance of understanding the current limits of AI and the inherent biases of the technology. The authors further discuss the ethical considerations of the use of AI in surgical training and in clinical use, with an emphasis on vascular surgery.

Taking the Pulse of the Current State of Simulation

Simulation in health-care professions has grown in the last few decades. We provide an overview of the history of simulation in other fields, the trajectory of simulation in health professions education, and research in medical education, including the learning theories and tools to assess and evaluate simulation programs. We also propose future directions for simulation and research in health professions education.

Tracking and evaluating motion skills in laparoscopy with inertial sensors

BACKGROUND

Analysis of surgical instrument motion is applicable in surgical skill assessment and monitoring of the learning progress in laparoscopy. Current commercial instrument tracking technology (optical or electromagnetic) has specific limitations and is expensive. Therefore, in this study, we apply inexpensive, off-the-shelf inertial sensors to track laparoscopic instruments in a training scenario.

METHODS

We calibrated two laparoscopic instruments to the inertial sensor and investigated its accuracy on a 3D-printed phantom. In a user study during a one-week laparoscopy training course with medical students and physicians, we then documented and compared the training effect in laparoscopic tasks on a commercially available laparoscopy trainer (Laparo Analytic, Laparo Medical Simulators, Wilcza, Poland) and the newly developed tracking setup.

RESULTS

Eighteen participants (twelve medical students and six physicians) participated in the study. The student subgroup showed significantly poorer results for the count of swings (CS) and count of rotations (CR) at the beginning of the training compared to the physician subgroup (p = 0.012 and p = 0.042). After training, the student subgroup showed significant improvements in the rotatory angle sum, CS, and CR (p = 0.025, p = 0.004 and p = 0.024). After training, there were no significant differences between medical students and physicians. There was a strong correlation between the measured learning success (LS) from the data of our inertial measurement unit system (LS_IMU) and the Laparo Analytic (LS_Lap) (Pearson's r = 0.79).

CONCLUSION

In the current study, we observed a good and valid performance of inertial measurement units as a possible tool for instrument tracking and surgical skill assessment. Moreover, we conclude that the sensor can meaningfully examine the learning progress of medical students in an ex-vivo setting.

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.

Feasibility of tracking in open surgical simulation

The aim of this study was to develop an adequate tracking method for open surgical training, using tracking of the instrument or hand motions.

An open surgical training model and the SurgTrac application were used to track four separate suturing tasks. These tasks were performed with colour markings of either instruments or fingers, to find the most promising setting for reliable tracking.

Four experiments were used to find the optimal settings for the tracking system. Tracking of instruments was not usable for knot tying by hand. Tracking of fingers seemed to be a more promising method. Tagging the fingers with a coloured balloon-tube, seemed to be a more promising method (1.2–3.0% right hand vs. 9.2–17.9% left hand off-screen) than covering the nails with coloured tape (1.5–3.5% right hand vs. 25.5–55.4% left hand off-screen). However, analysis of the videos showed that redness of the hand was seen as red tagging as well. To prevent misinterpreting of the red tag by redness of the hand, white surgical gloves were worn underneath in the last experiment. The off-screen percentage of the right side decreased from 1.0 to 1.2 without gloves to 0.8 with gloves and the off-screen percentage of the left side decreased from 16.9–17.9 to 6.6–7.2, with an adequate tracking mark on the video images.

This study shows that tagging of the index fingers with a red (right) and blue (left) balloon-tube while wearing surgical gloves is a feasible method for tracking movements during basic open suturing tasks.

The Potential of AR Solutions for Behavioral Learning: A Scoping Review

In recent years, educational researchers and practitioners have become increasingly interested in new technologies for teaching and learning, including augmented reality (AR). The literature has already highlighted the benefit of AR in enhancing learners’ outcomes in natural sciences, with a limited number of studies exploring the support of AR in social sciences. Specifically, there have been a number of systematic and scoping reviews in the AR field, but no peer-reviewed review studies on the contribution of AR within interventions aimed at teaching or training behavioral skills have been published to date. In addition, most AR research focuses on technological or development issues. However, limited studies have explored how technology affects social experiences and, in particular, the impact of using AR on social behavior. To address these research gaps, a scoping review was conducted to identify and analyze studies on the use of AR within interventions to teach behavioral skills. These studies were conducted across several intervention settings. In addition to this research question, the review reports an investigation of the literature regarding the impact of AR technology on social behavior. The state of the art of AR solutions designed for interventions in behavioral teaching and learning is presented, with an emphasis on educational and clinical settings. Moreover, some relevant dimensions of the impact of AR on social behavior are discussed in more detail. Limitations of the reviewed AR solutions and implications for future research and development efforts are finally discussed.

The Development of Artificial Intelligence in Hernia Surgery: A Scoping Review

Background

Artificial intelligence simulates human intelligence in machines that have undergone programming to make them think like human beings and imitate their activities. Artificial intelligence has dominated the medical sector to perform various patient diagnosis activities and improve communication between professionals and patients. The main goal of this study is to perform a scoping review to evaluate the development of artificial intelligence in all forms of hernia surgery except the diaphragm and upside-down hernia.

Methods

The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR) to guide the structuring of the manuscript and fulfill all the requirements of every subheading. The sources used to gather data are the PubMed, Cochrane, and EMBASE databases, IEEE and Google and Google Scholar search engines. AMSTAR tool is the most appropriate for assessing the methodological quality of the included studies.

Results

The study exclusively included twenty articles, whereby seven focused on artificial intelligence in inguinal hernia surgery, six focused on abdominal hernia surgery, five on incisional hernia surgery, and two on AI in medical imaging and robotics in hernia surgery.

Conclusion

The outcomes of this study reveal a significant literature gap on artificial intelligence in hernia surgery. The results also indicate that studies focus on inguinal hernia surgery more than any other types of hernia surgery since the articles addressing the topic are more. The study implies that more research is necessary for the field to develop and enjoy the benefits associated with AI. Thus, this situation will allow the integration of AI in activities like medical imaging and surgeon training.

Fuzzy logic supervisor –A surgical skills assessment system using multi-class detection of laparoscopic box-trainer instruments

Recent developments in deep learning can be used in skill assessments for laparoscopic surgeons. In Minimally Invasive Surgery (MIS), surgeons should acquire many skills before carrying out a real operation. The Laparoscopic Surgical Box-Trainer allows surgery residents to train on specific skills that are not traditionally taught to them. This study aims to automatically detect the tips of laparoscopic instruments, localize a point, evaluate the detection accuracy to provide valuable assessment and expedite the development of surgery skills and assess the trainees’ performance using a Multi-Input-Single-Output Fuzzy Logic Supervisor system. The output of the fuzzy logic assessment is the performance evaluation for the surgeon, and it is quantified in percentages. Based on the experimental results, the trained SSD Mobilenet V2 FPN can identify each instrument at a score of 70% fidelity. On the other hand, the trained SSD ResNet50 V1 FPN can detect each instrument at the score of 90% fidelity, in each location within a region of interest, and determine their relative distance with over 65% and 80% reliability, respectively. This method can be applied in different types of laparoscopic tooltip detection. Because there were a few instances when the detection failed, and the system was designed to generate pass-fail assessment, we recommend improving the measurement algorithm and the performance assessment by adding a camera to the system and measuring the distance from multiple perspectives.

Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions

OBJECTIVE

To synthesize peer-reviewed evidence related to the use of artificial intelligence (AI) in surgical education DESIGN: We conducted and reported a scoping review according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis with extension for Scoping Reviews guideline and the fourth edition of the Joanna Briggs Institute Reviewer's Manual. We systematically searched eight interdisciplinary databases including MEDLINE-Ovid, ERIC, EMBASE, CINAHL, Web of Science: Core Collection, Compendex, Scopus, and IEEE Xplore. Databases were searched from inception until the date of search on April 13, 2021.

SETTING/PARTICIPANTS

We only examined original, peer-reviewed interventional studies that self-described as AI interventions, focused on medical education, and were relevant to surgical trainees (defined as medical or dental students, postgraduate residents, or surgical fellows) within the title and abstract (see Table 2). Animal, cadaveric, and in vivo studies were not eligible for inclusion.

RESULTS

After systematically searching eight databases and 4255 citations, our scoping review identified 49 studies relevant to artificial intelligence in surgical education. We found diverse interventions related to the evaluation of surgical competency, personalization of surgical education, and improvement of surgical education materials across surgical specialties. Many studies used existing surgical education materials, such as the Objective Structured Assessment of Technical Skills framework or the JHU-ISI Gesture and Skill Assessment Working Set database. Though most studies did not provide outcomes related to the implementation in medical schools (such as cost-effective analyses or trainee feedback), there are numerous promising interventions. In particular, many studies noted high accuracy in the objective characterization of surgical skill sets. These interventions could be further used to identify at-risk surgical trainees or evaluate teaching methods.

CONCLUSIONS

There are promising applications for AI in surgical education, particularly for the assessment of surgical competencies, though further evidence is needed regarding implementation and applicability.

Applications of artificial intelligence in the diagnosis and prediction of erectile dysfunction: a narrative review

Despite the high prevalence of erectile dysfunction, patients are reluctant to seek medical advice, which leads to low diagnostic rates in clinical practice. Artificial intelligence has been widely applied in the diagnosis of many diseases and may alleviate the situation. However, the applications of artificial intelligence in erectile dysfunction have not been reviewed to date. Therefore, the assistance from artificial intelligence needs to be summarized. In this review, 418 publications before January 10, 2021, regarding artificial intelligence applications in diagnosing and predicting erectile dysfunction, were retrieved from five databases, including PubMed, EMBASE, the Cochrane Library, and two Chinese databases (WANFANG and CNKI). In addition, the reference lists of the included studies or relevant reviews were checked to avoid bias. Finally, 30 articles were reviewed to summarize the current status, merits, and limitations of applying artificial intelligence in diagnosing and predicting erectile dysfunction. The results showed that artificial intelligence contributed to developing novel diagnostic questionnaires, equipment, expert systems, classifiers by images and predictive models. However, most of the included studies were not subjected to external validations, resulting in doubt on the generalizability. In the future, more rigorously designed studies with high-quality datasets for erectile dysfunction are required.

Exploring the roles of artificial intelligence in surgical education: A scoping review

BACKGROUND

Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education.

METHODS

Nine bibliographic databases were searched from January 2010 to January 2021. Full-text articles were included if they focused on AI in surgical education.

RESULTS

Out of 14,008 unique sources of evidence, 93 were included. Out of 93, 84 were conducted in the simulation setting, and 89 targeted technical skills. Fifty-six studies focused on skills assessment/classification, and 36 used multiple AI techniques. Also, increasing sample size, having balanced data, and using AI to provide feedback were major future directions mentioned by authors.

CONCLUSIONS

AI can help optimize the education of trainees and our results can help educators and researchers identify areas that need further investigation.

Real-Time 3D Tracking of Laparoscopy Training Instruments for Assessment and Feedback

Assessment of minimally invasive surgical skills is a non-trivial task, usually requiring the presence and time of expert observers, including subjectivity and requiring special and expensive equipment and software. Although there are virtual simulators that provide self-assessment features, they are limited as the trainee loses the immediate feedback from realistic physical interaction. The physical training boxes, on the other hand, preserve the immediate physical feedback, but lack the automated self-assessment facilities. This study develops an algorithm for real-time tracking of laparoscopy instruments in the video cues of a standard physical laparoscopy training box with a single fisheye camera. The developed visual tracking algorithm recovers the 3D positions of the laparoscopic instrument tips, to which simple colored tapes (markers) are attached. With such system, the extracted instrument trajectories can be digitally processed, and automated self-assessment feedback can be provided. In this way, both the physical interaction feedback would be preserved and the need for the observance of an expert would be overcome. Real-time instrument tracking with a suitable assessment criterion would constitute a significant step towards provision of real-time (immediate) feedback to correct trainee actions and show them how the action should be performed. This study is a step towards achieving this with a low cost, automated, and widely applicable laparoscopy training and assessment system using a standard physical training box equipped with a fisheye camera.

Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications

Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among the available solutions, Raspberry Pi represents one of the most used SBCs. In the present work, two setups based on Raspberry Pi and its CMOS-based camera (a 3D-printed device and an adaptation of a commercial product named We-Lab) were investigated as diagnostic instruments. Different camera elaboration processes were investigated, showing how direct access to the 10-bit raw data acquired from the sensor before downstream imaging processes could be beneficial for photometric applications. The developed solution was successfully applied to the evaluation of the oxidative stress using two commercial kits (d-ROM Fast; PAT). We suggest the analysis of raw data applied to SBC and MCB platforms in order to improve results.

Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks

Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train the CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labeled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.

Current methods for assessing technical skill in cataract surgery

Surgery is a major source of errors in patient care. Preventing complications from surgical errors in the operating room is estimated to lead to reduction of up to 41 846 readmissions and save $620.3 million per year. It is now established that poor technical skill is associated with an increased risk of severe adverse events postoperatively and traditional models to train surgeons are being challenged by rapid advances in technology, an intensified patient-safety culture, and a need for value-driven health systems. This review discusses the current methods available for evaluating technical skills in cataract surgery and the recent technological advancements that have enabled capture and analysis of large amounts of complex surgical data for more automated objective skills assessment.

The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs

Data science is an inter-disciplinary field that uses computer-based algorithms and methods to gain insights from large and often complex datasets. Data science, which includes Artificial Intelligence techniques such as Machine Learning (ML), has been credited with the promise to transform Health Professions Education (HPE) by offering approaches to handle big (and often messy) data. To examine this promise, we conducted a critical review to explore: (1) published applications of data science and ML in HPE literature and (2) the potential role of data science and ML in shifting theoretical and epistemological perspectives in HPE research and practice. Existing data science studies in HPE are often not informed by theory, but rather oriented towards developing applications for specific problems, uses, and contexts. The most common areas currently being studied are procedural (e.g., computer-based tutoring or adaptive systems and assessment of technical skills). We found that epistemic beliefs informing the use of data science and ML in HPE poses a challenge for existing views on what constitutes objective knowledge and the role of human subjectivity for instruction and assessment. As a result, criticisms have emerged that the integration of data science in the field of HPE is in danger of becoming technically driven and narrowly focused in its approach to teaching, learning and assessment. Our findings suggest that researchers tend to formalize around the epistemological stance driven largely by traditions of a research paradigm. Future data science studies in HPE need to involve both education scientists and data scientists to ensure mutual advancements in the development of educational theory and practical applications. This may be one of the most important tasks in the integration of data science and ML in HPE research in the years to come.

Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review

BACKGROUND

The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice.

OBJECTIVE

The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience.

METHODS

A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments.

RESULTS

This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula.

CONCLUSIONS

To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.

Assessing a new coarctation repair simulator based on real patient's anatomy

OBJECTIVES

To perform the preliminary tests of coarctation of aorta repair trainer, evaluate the surgical properties of the simulation and to assess and enhance residents' skills.

METHODS

Single patient's angio-CT anatomy data were converted into magnified 3D-printed model of aortic coarctation with hypoplastic aortic arch, serving for creation of a mould used during wax copies casting. Wax cores were painted with six layers of elastic silicone and melted, yielding phantoms that were consecutively fixed in a mounting with and without a thoracic wall. Simulation included: proximal and distal aortic arch clamping, incision of its lesser curvature, extended end-to-end anastomosis with 7-0 suture. A head-mounted camera video recording enabled anastomosis time and mean one suture bite time evaluation. Leakage assessment was done by a water test.

RESULTS

Two residents performed nine simulations each. Last four runs were performed with thoracic wall attached. All phantoms performed well, enabling tissue-like handling and cutting, excellent suture retention, and satisfactory elasticity. Median anastomosis times were 22'33″ and 24'47″ for phantoms without and with thoracic wall (p = not significant (NS)). Median times needed to pass suture through one side of anastomosis and regrasp needle were, respectively, 9″ and 13″ (p < 0.001). Median total number of leakages per phantom equalled 2 for both difficulty levels. There were no significant inter-resident differences in all assessed parameters.

CONCLUSIONS

This medium-fidelity aortic coarctation repair trainer showed its feasibility in replication of major critical steps of the real operation. Objective surgical efficiency parameters could be obtained from each simulation and compared between trainees and at different adjustable difficulty levels.

Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review

Background

Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education.

Objective

This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education.

Methods

Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively.

Results

A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students’ learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications.

Conclusions

The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students’ learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.