Envisioning gamification in anesthesia, pain management, and critical care: basic principles, integration of artificial intelligence, and simulation strategies

Game on or game over? Gamification from 360-degree perspective, perception, confidence, and challenges in simulation based nursing education: mixed-method study

BACKGROUND

Gamification has emerged as a transformative approach in nursing education, especially within simulation-based learning environments. It is recognized for enhancing student engagement, knowledge retention, and confidence. Despite its potential, limited research has explored the perceptions and confidence of nurse educators and students, as well as the challenges encountered during its implementation. The study aimed to assess the perceptions and confidence of nurse educators and nursing students towards integrating gamification into simulation-based nursing education, identify implementation barriers, and develop and validate two psychometric tools: the Gamification Perception Assessment Tool and the Nurse Educator Confidence Tool.

METHODS

A convergent mixed-methods design was utilized, involving 115 nurse educators and 317 nursing students from eight nursing institutions in Cairo. Quantitative data were collected using the newly developed tools, which underwent rigorous validation through Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and reliability testing. Qualitative data were collected via semi-structured questionnaires and interviews with nurse educators and analyzed thematically to explore implementation challenges.

RESULTS

The overall mean perception score was 34.8 ± 8.4 for nursing students and 36.3 ± 7.9 for nurse educators, with the majority of participants in both groups showing a high perception level (61.7% for educators and 58.9% for students). Nurse educators displayed moderate to high confidence, which was significantly influenced by their experience and prior training. A strong positive correlation (r = 0.711, p = 0.001) was found between perception and confidence. The psychometric tools demonstrated high reliability (Cronbach's α = 0.68-0.85) and model fit. Thematic analysis revealed barriers such as institutional policy gaps, limited IT support, and lack of training.

CONCLUSION

Gamification is positively perceived and fosters educator confidence in simulation-based nursing education. However, successful implementation requires institutional support, faculty training, and standardized evaluation tools to overcome existing challenges and optimize educational outcomes. The study provides validated tools and empirical insights into gamification in nursing education.

CLINICAL TRIAL NUMBER

Not applicable.

Bridging the gap between scientists and clinicians: addressing collaboration challenges in clinical AI integration

This article explores challenges for bridging the gap between scientists and healthcare professionals in artifical intelligence (AI) integration. It highlights barriers, the role of interdisciplinary research centers, and the importance of diversity, equity, and inclusion. Collaboration, education, and ethical AI development are essential for optimizing AI's impact in perioperative medicine.

Multi-layered data framework for enhancing postoperative outcomes and anaesthesia management through natural language processing

Anaesthesia management is a critical aspect of perioperative care, directly influencing postoperative recovery, pain management, and patient outcomes. Despite advancements in anaesthesia techniques, variability in patient responses and unexpected postoperative complications remain significant challenges. The research proposes a multi-layered architecture named Anaesthesia CareNet for analyzing data from diverse sources to enhance personalized anaesthesia management and postoperative outcome prediction. The architecture is structured into two primary layers: Data processing and Predictive Modeling. In the Data processing layer, advanced Natural Language Processing (NLP) techniques such as Named Entity Recognition (NER), normalization, lemmatization, and stemming are applied to clean and standardize the unstructured clinical data. Generative Pre-trained Transformer 3 (GPT-3), a Large Language Model (LLM) is employed as a feature extraction method, allowing the system to process and analyze complex clinical narratives and unstructured textual data from patient records. This enables more precise and personalized predictions, not only improving anaesthesia management but also laying the groundwork for broader applications in life sciences. The extracted data is passed into the predictive modeling layer, where the Intelligent Golden Eagle Fine-Tuned Logistic Regression (IGE-LR) model is applied. By analyzing correlations between patient characteristics, surgical details, and postoperative recovery patterns, IGE-LR enables the prediction of complications, pain management requirements, and recovery trajectories beyond anaesthesia; the methodology has potential applications in diverse areas such as diagnostics, drug discovery, and personalized medicine, where large-scale data analysis, predictive modeling, and real-time adaptability are crucial for improving patient outcomes. The proposed IGE-LR method achieves higher performance with 91.7 % accuracy, 90.6 % specificity, and 90 % AUC, with a recall of 91.3 %, precision of 90.1 %, and an F1-Score of 90.4 %. By leveraging advanced NLP and predictive analytics, Anaesthesia CareNet exemplifies how AI-driven frameworks can transform life sciences, advancing personalized healthcare and creating a more precise, efficient, and dynamic approach to treatment management.

Comparative analysis of traditional and integrated approaches to radiology training for residents

BACKGROUND

The study aims to conduct a comparative analysis of traditional and integrated approaches to radiology teaching in order to evaluate the effectiveness of novel educational methods.

METHODS

The study was conducted in Shenzhen, China, between January and December 2023. It involved 100 radiology residents who were randomly assigned to either a traditional training (TT) group or an integrated training (IT) group. The average age of participants was 28 years.

RESULTS

The TT group received conventional lectures and practical training, while the IT group used simulation software, interactive platforms, and artificial intelligence (AI) tools. The analysis revealed that the mean score of the IT group in the theoretical knowledge test was 170.3 ± 15.1, which is significantly higher than that of the TT group (155.7 ± 20.4; t = 4.21, p < 0.001). In the practical skills test, the IT group scored 160.7 ± 22.4, while the TT group scored 135.8 ± 25.6 (t = 5.13, p < 0.001).

CONCLUSIONS

The findings of the study indicate a significant advantage of an integrated approach to radiology teaching over conventional methods. The integration of modern technologies into the learning process has been shown to enhance both short- and long-term educational outcomes in medical education. This finding is of practical significance for educational institutions in this field. It is recommended that integrated teaching methods be introduced in order to improve the quality of specialist training.

CLINICAL TRIAL NUMBER

Not applicable.

Comparative Efficacy of Video Games Versus Midazolam in Reducing Perioperative Anxiety in Pediatric Patients: Systematic Review and Meta-Analysis

Background

Pediatric patients undergoing surgery frequently experience significant anxiety, which can result in adverse effects such as prolonged sedation and behavioral changes associated with pharmacological interventions such as oral midazolam. Video games offer a nonpharmacological distraction method that shows promise in alleviating procedural anxiety without significant adverse effects. However, the effectiveness of video games compared to midazolam in managing perioperative anxiety remains uncertain.

Objective

This study aimed to evaluate the effectiveness of video game interventions in reducing perioperative anxiety in pediatric patients undergoing general anesthesia.

Methods

We conducted a comprehensive search across PubMed, Embase, Web of Science, and the Cochrane Library, supplemented by reference screening. Primary outcomes included anxiety levels assessed during parent separation and mask induction procedures, while secondary outcomes encompassed emergence delirium, postoperative behavior, and length of stay in the postanesthesia care unit (PACU). The risk of bias was assessed using the Risk of Bias 2 scale. Data were synthesized descriptively and through meta-analysis, with the certainty of the evidence evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria.

Results

Six randomized controlled trials involving 612 participants were included in the analysis. Children who participated in video game interventions reported significantly lower anxiety levels during parent separation (standardized mean difference, SMD -0.31, 95% CI -0.50 to -0.12; P=.001), with high certainty, and during mask induction (SMD -0.29, 95% CI -0.52 to -0.05; P=.02), with moderate certainty, compared to those receiving oral midazolam. Additionally, significant differences in postoperative behavior changes in children were observed compared to oral midazolam (SMD -0.35, 95% CI -0.62 to -0.09; P=.008). Children in the video game intervention groups also had a shorter length of stay in the PACU (mean difference, MD -19.43 min, 95% CI -31.71 to -7.16; P=.002). However, no significant differences were found in emergence delirium (MD -2.01, 95% CI -4.62 to 0.59; P=.13).

Conclusions

Video game interventions were more effective than midazolam in reducing perioperative anxiety among pediatric patients, improving postoperative behavior, and shortening the length of stay in the PACU. However, video games alone did not outperform midazolam in managing emergence delirium. Further high-quality research is needed for more conclusive results.

Bridging theory and practice: a scoping review protocol on gamification's impact in clinical reasoning education

INTRODUCTION

In the rapidly evolving field of medical education, gamification has emerged as a promising strategy to enhance clinical reasoning skills among healthcare professionals. By incorporating game-like elements into the learning environment, gamification strives to enhance engagement, motivation and knowledge retention. Given the importance of clinical reasoning in medical decision-making and patient care, this scoping review protocol aims to systematically explore developments, implementations and outcomes of gamification in clinical reasoning education.

METHODS AND ANALYSIS

The scoping review will follow the Arksey and O'Malley methodological framework, enhanced by guidelines from the Joanna Briggs Institute. We will search four major databases: OVID Medline, Scopus and Web of Science using key terms such as "gamification," "clinical reasoning," and "medical education". Studies will be selected based on the participants, concepts and contexts (PCC) framework, focusing on literature published in English. Two independent reviewers will screen studies and extract data on gamification elements used in clinical reasoning education. Any disagreement between the reviewers will be resolved by consulting a third person. We will provide a narrative synthesis of the findings, highlighting the variety of gamified strategies and their effects on clinical reasoning skills. This review will also map out gaps in the current literature and provide direction for future research.

ETHICS AND DISSEMINATION

The scoping review, which aggregates and synthesises publicly available studies, does not require ethics approval due to its nature as a compilation of existing research. The reporting of findings will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist, promoting both thoroughness and transparency in our analysis. Our dissemination plan encompasses publication in a peer-reviewed journal and presentations at academic conferences focused on medical education. This strategy is designed to engage educators, curriculum designers and policymakers within the sector, ensuring our insights reach those who can apply them most effectively.

Dissecting the link between PD-1/PD-L1-based immunotherapy and cancer pain: mechanisms, research implications, and artificial intelligence perspectives

Cancer-related pain represents one of the most common complaints of cancer patients especially for those with advanced-stage of disease and/or bone metastases. More effective therapeutic strategies are needed not only to improve the survival of cancer patients but also to relieve cancer-related pain. In the last decade, immune checkpoint inhibitor (ICI)-based immunotherapy targeting programmed cell death-1 (PD-1) and its ligand 1 (PD-L1) has revolutionized cancer care. Beyond its anticancer role, PD-1/PD-L1 axis pathway is involved in many other physiological processes. PD-L1 expression is found in both malignant tissues and normal tissues including the dorsal root ganglion, and spinal cord. Through its interaction with PD-1, PD-L1 can modulate neuron excitability, leading to the suppression of inflammatory, neuropathic, and bone cancer pain. Therefore, since the intricate relationship between immunotherapy and pain should be largely dissected, this comprehensive review explores the complex relationship between PD-1/PD-L1-based immunotherapy and cancer-related pain. It delves into the potential mechanisms through which PD-1/PD-L1 immunotherapy might modulate pain pathways, including neuroinflammation, neuromodulation, opioid mechanisms, and bone processes. Understanding these mechanisms is crucial for developing future research directions in order to optimize pain management strategies in cancer patients. Finally, this article discusses the role of artificial intelligence (AI) in advancing research and clinical practice in this context. AI-based strategies, such as analyzing large datasets and creating predictive models, can identify patterns and correlations between PD-1/PD-L1 immunotherapy and pain. These tools can assist healthcare providers in tailoring treatment plans and pain management strategies to individual patients, ultimately improving outcomes and quality of life for those undergoing PD-1/PD-L1-based immunotherapy.

Gamification, Serious Games, and Simulation in Health Professions Education

Health care educators may enhance learning with thoughtful incorporation of game elements. Gamification has shown success across various fields in medical education. It has demonstrated deeper engagement by leveraging both intrinsic and extrinsic motivational factors. While beneficial, gamification requires thoughtful implementation to increase active learning and avoid potential negative effects, such as unhealthy competition. Serious games integrate learning objectives directly within their framework, making the educational experience an intrinsic part of gameplay. These games are specifically designed to enhance knowledge and skills while promoting decision making, teamwork, and communication. The immersive nature of serious games requires players to actively engage and apply their knowledge to solve complex problems. Serious games and simulation represent transformative educational approaches that not only enhance learning and retention but also develop essential competencies crucial for health care professionals. These strategies, when combined with effective debriefing, provide a robust framework to enrich education and training in health care. [Pediatr Ann. 2024;53(11):e401-e407.].

Pain Management in Cancer Patients: The Effectiveness of Digital Game-based Interventions: A Rapid Literature Review

OBJECTIVES

Pain is a common side effect of cancer that negatively impacts biopsychosocial well-being and quality of life. There has been increasing interest in using digital game interventions for managing pain in cancer patients. The present study aimed to consolidate and summarize knowledge regarding the role of games in reducing pain among cancer patients and enhancing their overall quality of life.

METHODS

We reviewed studies published between 2000 and April 8, 2023, from databases such as PubMed, Scopus, and Web of Science. The focus was on determining the impact of health games on pain management in cancer patients.

RESULTS

An initial search identified 2,544 studies, which were narrowed down to 10 relevant articles after removing duplicates and assessing quality. These studies examined the use of mobile and computer games across various types of cancer, including both pediatric and adult cases. The findings indicate that digital games, particularly those utilizing virtual reality technologies, can diminish pain and anxiety while enhancing treatment outcomes. Overall, the application of these technologies has the potential to improve cancer treatment.

CONCLUSIONS

Digital game interventions empower cancer patients by fostering effective communication and patient-centered approaches, which enhance perceptions, outcomes, and overall well-being. These games provide real-time feedback and facilitate interaction with healthcare professionals, which promotes self-management and boosts patient motivation and adherence to treatment protocols. As personalized educational platforms, they increase engagement through educational resources and symptom tracking, while also encouraging physical activity. Furthermore, they act as distraction tools during painful procedures, presenting new research opportunities in pain management and enhancing overall quality of life.

A mobile serious game about diabetes self-management: Design and evaluation

Type 2 Diabetes Mellitus (T2DM) is a chronic condition that requires ongoing self-management and education. In recent years, there has been a growing interest in utilizing mobile serious games as a tool for patient education and engagement. This article presents the development of DiaPo, a mobile serious game designed to improve self-management education for patients with T2DM. DiaPo integrates gamification techniques to increase patient engagement and motivation while providing essential information about disease management. The development of DiaPo followed a structured design process, utilizing the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) educational system. This systematic approach allowed for the integration of best practices in educational game design and diabetes care. The development team consisted of experts in medical informatics, game design, and diabetes care, ensuring a multidisciplinary approach to the game's creation. The game's narrative focuses on a T2DM patient who earns positive points for making healthy lifestyle choices and negative points for poor ones. This gamified approach aims to reinforce positive behaviors and provide immediate feedback on negative ones. Interactive animations confirm or deny options selected by the player, further enhancing the learning experience. DiaPo offers a flexible and adaptable platform suitable for diverse audiences, promoting inclusiveness and accessibility in T2DM education. DiaPo represents a novel approach to self-management education for patients with T2DM, utilizing gamification techniques and a multidisciplinary design process to create an engaging and informative mobile serious game. By promoting inclusiveness and accessibility, DiaPo has the potential to empower patients with T2DM to take an active role in their disease management. As the field of mobile serious games continues to evolve, DiaPo stands as a promising tool for improving T2DM education and patient outcomes.

Recent Advances and Perspectives in Anesthesiology: Towards Artificial Intelligence-Based Applications

In recent years, the field of anesthesiology has seen remarkable advancements in patient safety, comfort, and outcomes [...].

Assessment of Ultrasound Utilization for Peripheral Venous Access in Nursing Practices. An Observational Study From an Italian University Hospital

Background

Establishing peripheral intravenous access can be challenging, often resulting in care delays, increased complications, and higher healthcare costs. While ultrasound-guided techniques have shown potential in improving success rates and reducing complications, their utilization by nurses varies significantly.

Aim

This study aims to evaluate the efficacy and utilization of ultrasound for difficult peripheral venous access among nurses at an Italian university hospital.

Methods

Data were collected over six months from 64 nurses across various units. The study assessed the prevalence of ultrasound training, additional education, and the frequency of ultrasound use for venipuncture and tip navigation.

Results

Of the 64 nurses, 60.9% had received prior ultrasound training, with 50% undergoing further education. Despite this, only 50% regularly used ultrasound for venipuncture, and a mere 26.6% employed it for tip navigation.

Conclusion

Enhanced education and training are essential for increasing the utilization of ultrasound techniques among nurses. This, in turn, can optimize patient outcomes and enhance safety in clinical settings.

Regional Anaesthesia, Contemporary Techniques, and Associated Advancements: A Narrative Review

In particular, the application of regional anaesthesia techniques in existing medicine can be characterized as experiencing regular changes in recent decades. It is useful for obtaining accurate and efficient pain management solutions, from the basic spinal and epidural blocks to the novel ultrasound nerve blocks and constant catheter procedures. These advancements do enhance not only the value of the perioperative period but also the patient's rated optimization as enhancing satisfaction, better precision, and the safety of nerve block placement. The use of ultrasound technology makes it even easier to determine the proper positioning of the needle and to monitor nerve block placement. Moreover, the duration and efficiency of regional anaesthesia are being enhanced by state-of-the-art approaches, which come in the form of liposomal bupivacaine, and better recovery plans and protocols, which shorten recovery time and decrease the number of hospital days. As these methods develop further, more improvements in the safety, efficacy, and applicability of regional anaesthesia in contemporary medicine are anticipated through continued research and innovation.

Advances in pediatric perioperative care using artificial intelligence

PURPOSE OF THIS REVIEW

This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, controlling anesthetic depth and nociception during surgery, and contributing to the training of pediatric anesthesia providers.

RECENT FINDINGS

The use of AI in healthcare has increased in recent years, largely due to the accessibility of large datasets, such as those gathered from electronic health records. Although there has been less focus on pediatric anesthesia compared to adult anesthesia, research is on- going, especially for applications focused on risk factor identification for adverse perioperative events. Despite these advances, the lack of formal external validation or feasibility testing results in uncertainty surrounding the clinical applicability of these tools.

SUMMARY

The goal of using AI in pediatric anesthesia is to assist clinicians in providing safe and efficient care. Given that children are a vulnerable population, it is crucial to ensure that both clinicians and families have confidence in the clinical tools used to inform medical decision- making. While not yet a reality, the eventual incorporation of AI-based tools holds great potential to contribute to the safe and efficient care of our patients.

Catheterized chicken for training on ultrasound-guided vascular access: A simple, cost-effective, and effective model

Ultrasound-guided vascular access is a medical procedure that is becoming increasingly common in daily practice and is recommended to avoid iatrogenic complications. One of the procedures with a high-risk rate of complications is the vascular puncture. However, training on this technique can be challenging due to the limited availability of simulation models. We propose a simple, cost-effective, and effective ultrasound-guided vascular access simulation model that utilizes chicken breast and a urine catheter to address this need.

Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education

The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.

Large Language Models in Oncology: Revolution or Cause for Concern?

The technological capability of artificial intelligence (AI) continues to advance with great strength. Recently, the release of large language models has taken the world by storm with concurrent excitement and concern. As a consequence of their impressive ability and versatility, their provide a potential opportunity for implementation in oncology. Areas of possible application include supporting clinical decision making, education, and contributing to cancer research. Despite the promises that these novel systems can offer, several limitations and barriers challenge their implementation. It is imperative that concerns, such as accountability, data inaccuracy, and data protection, are addressed prior to their integration in oncology. As the progression of artificial intelligence systems continues, new ethical and practical dilemmas will also be approached; thus, the evaluation of these limitations and concerns will be dynamic in nature. This review offers a comprehensive overview of the potential application of large language models in oncology, as well as concerns surrounding their implementation in cancer care.

Between human and AI: assessing the reliability of AI text detection tools

OBJECTIVE

Large language models (LLMs) such as ChatGPT-4 have raised critical questions regarding their distinguishability from human-generated content. In this research, we evaluated the effectiveness of online detection tools in identifying ChatGPT-4 vs human-written text.

METHODS

A two texts produced by ChatGPT-4 using differing prompts and one text created by a human author were analytically assessed using the following online detection tools: GPTZero, ZeroGPT, Writer ACD, and Originality.

RESULTS

The findings revealed a notable variance in the detection capabilities of the employed detection tools. GPTZero and ZeroGPT exhibited inconsistent assessments regarding the AI-origin of the texts. Writer ACD predominantly identified texts as human-written, whereas Originality consistently recognized the AI-generated content in both samples from ChatGPT-4. This highlights Originality's enhanced sensitivity to patterns characteristic of AI-generated text.

CONCLUSION

The study demonstrates that while automatic detection tools may discern texts generated by ChatGPT-4 significant variability exists in their accuracy. Undoubtedly, there is an urgent need for advanced detection tools to ensure the authenticity and integrity of content, especially in scientific and academic research. However, our findings underscore an urgent need for more refined detection methodologies to prevent the misdetection of human-written content as AI-generated and vice versa.

The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives

Within the domain of Natural Language Processing (NLP), Large Language Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through the scaling of model size, pretraining of corpora, and computational resources. The potential healthcare applications of these models primarily involve chatbots and interaction systems for clinical documentation management, and medical literature summarization (Biomedical NLP). The challenge in this field lies in the research for applications in diagnostic and clinical decision support, as well as patient triage. Therefore, LLMs can be used for multiple tasks within patient care, research, and education. Throughout 2023, there has been an escalation in the release of LLMs, some of which are applicable in the healthcare domain. This remarkable output is largely the effect of the customization of pre-trained models for applications like chatbots, virtual assistants, or any system requiring human-like conversational engagement. As healthcare professionals, we recognize the imperative to stay at the forefront of knowledge. However, keeping abreast of the rapid evolution of this technology is practically unattainable, and, above all, understanding its potential applications and limitations remains a subject of ongoing debate. Consequently, this article aims to provide a succinct overview of the recently released LLMs, emphasizing their potential use in the field of medicine. Perspectives for a more extensive range of safe and effective applications are also discussed. The upcoming evolutionary leap involves the transition from an AI-powered model primarily designed for answering medical questions to a more versatile and practical tool for healthcare providers such as generalist biomedical AI systems for multimodal-based calibrated decision-making processes. On the other hand, the development of more accurate virtual clinical partners could enhance patient engagement, offering personalized support, and improving chronic disease management.

Learner-Centered Experience-Based Medical Education in an AI-Driven Society: A Literature Review

This review proposes and explores the significance of "experience-based medical education" (EXPBME) in the context of an artificial intelligence (AI)-driven society. The rapid advancements in AI, particularly driven by deep learning, have revolutionized medical practices by replicating human cognitive functions, such as image analysis and data interpretation, significantly enhancing efficiency and precision across medical settings. The integration of AI into healthcare presents substantial potential, ranging from precise diagnostics to streamlined data management. However, non-technical skills, such as situational awareness on recognizing AI's fallibility or inherent risks, are critical for future healthcare professionals. EXPBME in a clinical or simulation environment plays a vital role, allowing medical practitioners to navigate AI failures through sufficient reflections. As AI continues to evolve, aligning educational frameworks to nurture these fundamental non-technical skills is paramount to adequately prepare healthcare professionals. Learner-centered EXPBME, combined with the AI literacy acquirement, stands as a key pillar in shaping the future of medical education.