Immersive virtual reality in nursing education: A scoping review of components, outcome variables, and interaction modes.

Abstract Background Artificial intelligence (AI) marks an inflection point in medical education systems built on scarcity of resources. These designs privilege standardisation and recall‐heavy examinations over reasoning and adaptive expertise, defined as the capacity to apply knowledge flexibly in uncertain clinical contexts, producing learners who memorise content but struggle with ambiguity, integration across domains and decision‐making under pressure. Objectives To outline a conceptual roadmap for integrating AI into medical education that strengthens adaptive expertise, productive struggle and assessment integrity rather than eroding them. Methods Conceptual analysis using educational, assessment and cognitive science frameworks to contrast scarcity‐era logics with emerging AI capabilities and synthesise illustrative use cases. Results We describe how AI can scaffold knowledge acquisition and inquiry; support authentic practice via virtual patients and educator‐created, AI‐enabled teaching tools and reshape assessment through blueprint‐aligned items and predictive learning analytics. We highlight AI's double‐edged nature: risks of undermining integrity, promoting cognitive deskilling and bypassing productive struggle, defined as purposeful, scaffolded difficulty that feels effortful yet achievable and that strengthens long‐term learning. We propose enabling conditions: trust, transparency, structured difficulty, and deliberate cognitive redistribution, defined as intentional reallocation of cognitive work between humans and AI tools, which offloads routine lower‐yield tasks to machines to preserve and advance human judgement, values, relationships and professional identity formation. Conclusions AI will either accelerate superficial shortcuts or amplify humane, expert practice, depending on how pedagogy, assessment and culture are redesigned. Intentional alignment can reclaim time and cognitive space for the uniquely human work at the heart of education.

This paper presents a digital technique for creating a dynamic edentulous virtual patient using a 3-dimensional (3D) printed multifunctional intraoral device. By integrating custom impression trays, occlusal rims, a gothic arch tracer, facial scan markers, and an optical jaw tracking device, this multifunctional device allows capturing essential diagnostic datasets in one visit and enables unified alignment within computer-aided design (CAD) software. This approach addresses current key limitations, such as the lack of stable tracking references and difficulties in aligning intraoral and facial scans.

Impact of simulated reduced field of view on AI-driven orofacial virtual patient creation

ABSTRACTDuring a large vessel occlusion, the survivability of the affected brain tissue depends on the ability of blood to reach the compromised territory. Consequently, the severity of ischemic strokes and the outcome of interventional treatments like thrombectomy are strongly influenced by individual anatomical features of the brain's vascular network, particularly its collateralization. However, analyzing the role of collateral circulation has proven particularly challenging, as it requires highly detailed models of arterial networks that include very small collateral vessels (~50 μm diameter). This article presents a computational framework for constructing realistic brain vascular models that capture the anatomical variability of both the circle of Willis and the pial collateral network. The methodology integrates image‐based vascular reconstruction, arterial tree extension via constrained constructive optimization, and generation of leptomeningeal collateral vessels. Blood flow simulations are performed using lumped parameter models, while virtual angiograms are generated through distributed compartment modeling of transport. A virtual patient population with variable collateralization is used to study the impact of anatomical differences on collateral flow and angiographic signatures in the presence of large vessel occlusions. The results show good agreement with in vivo data and highlight features that could help infer the level of collateralization from clinical angiograms. This framework offers a foundation for improving patient‐specific stroke treatment planning and understanding the hemodynamic implications of vascular variability.

The increasing adoption of Internet hospital systems—enabled by the real-time data streaming capabilities of the Internet of Medical Things (IoMT)—has intensified the need for secure, scalable, and low-latency data management infrastructures. Existing blockchain-based solutions often fail to meet these requirements, particularly under high-frequency workloads and stringent privacy demands. To address these limitations, this study proposes a simulation-based post-quantum-inspired alliance blockchain architecture tailored for Internet hospital systems. The framework incorporates four key innovations: (1) a Kyber-inspired hybrid encryption simulation, reducing encryption and decryption times by 72.3% and 74.4%, respectively, compared to RSA-2048; (2) a lightweight patient-centric access control mechanism based on authorization proofs achieving an average verification latency of ∼0.002 ms; (3) a Raft-based scalable consensus protocol, tested under a synchronous constant-delay network assumption, reducing consensus latency by 92.3% while supporting up to 1000 nodes with sub-150 ms finality; and (4) a fault-tolerant IoMT data ingestion layer using 3-of-5 median filtering, sustaining 90–96.2% sensor correction accuracy under varying fault injection rates. The system is prototyped in Google Colab Pro using synthetic data from 1000 virtual patients. Comparative benchmarks against PBFT and RSA-based systems show a fivefold increase in throughput, ∼9.4–12.3% energy savings per transaction, and ∼14% lower memory consumption during encryption. With a modest daily storage footprint (∼15 MB/day), the proposed solution is both resource-efficient and deployment-ready in simulation environments. These results confirm the potential of this architecture to enable trustworthy, energy-aware, and real-time blockchain infrastructures for next-generation digital healthcare ecosystems.

In silico prediction of hip fractures: improved fall modeling and expanded validation across cohorts with diverse risk profiles.

Evaluating a virtual simulation chatbot to improve communication for radiation therapy students: A pilot study.

To develop and evaluate the acceptability and effectiveness of an extended reality (XR) avatar-based menopause education simulation module for the clinical training of OB/GYN residents. This menopause education pilot recruited 57 OB/GYN residents from nine institutions via listservs and snowball sampling. Participants completed a menopause-focused XR avatar simulation module developed using Kern's curriculum design framework and guided by APGO/CREOG objectives and national guidelines. Learners completed an 11-item preintervention and postintervention assessment measuring usability and satisfaction (Kirkpatrick Level 1: reaction outcomes) and knowledge and self-reported confidence (Level 2: learning outcomes). Descriptive statistics summarized additional outcomes, and paired t tests were used to evaluate changes in knowledge. We also conducted a brief descriptive review of voluntary free-text comments. From preintervention to postintervention, mean knowledge scores improved from 79.8% to 82.8% (P=0.028). Participants reported improved preparedness and confidence across multiple domains: 59% improvement in overall readiness to manage menopause, 50% in comfort managing hormone therapy, and 39% in managing nonhormone therapy. No learners reported feeling "not at all prepared" postintervention. Most rated the module as effective (63%) and more engaging than lectures (60%); 96% would recommend it. Narrative feedback endorsed the module as valuable while suggesting improvements in interactivity, content depth, and avatar responsiveness. The XR simulation module demonstrated acceptability and effectiveness in enhancing clinical menopause training. Results support its potential as a scalable educational tool to address gaps in clinical menopause education.

Purpose: This scoping review aimed to explore the characteristics and educational effects of game-based virtual reality (VR) programs used in nursing education, providing foundational insights for future instructional design and research. Methods: Following the Joanna Briggs Institute guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist, a comprehensive literature search was conducted across eight databases for studies published between April 2014 and March 2025. Studies were screened and selected using the population, concept, context framework. Twenty-four studies met the inclusion criteria and were analyzed. Results: Most of the 24 included studies targeted nursing students, with some involving practicing nurses. Interventions primarily employed immersive VR head-mounted displays and incorporated diverse game elements such as mission-based scenarios, challenges, feedback loops, and virtual patient interactions. Educational effects were categorized into cognitive, affective, and psychomotor domains. Improvements in knowledge acquisition, self-efficacy, learning motivation, and skill performance were consistently reported. Additionally, most studies reported enhanced learner satisfaction, engagement, and emotional immersion. However, limited evidence was found regarding teamwork and communication skills. Conclusion: Game-based VR programs show substantial potential as effective instructional strategies in nursing education. Their design features—including immediate feedback, repeatable immersive scenarios, and engaging game mechanics—support learner-centered and self-directed learning. Nevertheless, the current evidence base largely focuses on nursing students and short-term outcomes. Future studies should encompass diverse nursing populations and assess long-term effects and real-world applicability, particularly in team-based and clinical practice contexts.

Computational model-informed comparative evaluation of botulinum toxin a formulations: A 20-year simulation of onset, diffusion, duration, and immunogenicity.

BackgroundGuideline-based therapy (GBT) drugs for Mycobacterium avium-complex (MAC) lung disease (LD) were chosen in part because they have low minimum inhibitory concentrations (MICs). Despite these low MICs, GBT achieves 6-month sustained sputum culture conversion in only 43% of patients.MethodsFirst, we co-incubated tigecycline with MAC for 7 days in time-kill studies and calculated the exposure mediating 50% of maximal effect (Emax), or EC50. Next, we performed tigecycline exposure-effect studies in the hollow fiber system of MAC (HFS-MAC) inoculated with the reference ATCC#700898 isolate. Third, we performed an exposure-effect study in the HFS-MAC inoculated with five clinical isolates. Finally, the target exposure (EC80) was used to identify a clinical dose of inhaled tigecycline for MAC-LD in 10,000 virtual subject Monte Carlo experiments (MCE).ResultsIn time-kill studies, the EC50 was 0–24 h area under the concentration-time curve-to-MIC (AUC0–24/MIC) of 174 for extracellular and 4.56 for intracellular MAC (p < 0.001). In the HFS-MAC inoculated with ATCC#700898, the EC50 statistically differed between sampling days. However, studies with five different isolates demonstrated a stable and robust day-to-day EC50 (%CV = 18.18%), with an EC80 AUC0–24/MIC of 33.65. The Emax was 4.84 log10 CFU/mL. In MCE, tigecycline inhalational doses of 35–40 mg/day achieved the EC80 target in >90% of virtual patients, with an MIC breakpoint of 256 mg/L.ConclusionInstead of static time-kill studies with a reference strain, inclusion of multiple MAC isolates in HFS-MAC studies improves the precision of pharmacokinetic/pharmacodynamic parameter estimates. Tigecycline administered via the inhalational route could contribute to the treatment of MAC-LD.

There is limited access to real patients during healthcare training, presenting a critical need for realistic, scalable simulations to develop students’ clinical reasoning skills. This study investigates how LLMs-supported virtual patient interactions impact healthcare students’ confidence in clinical reasoning and decision-making. We developed a web-based framework featuring a photorealistic patient named Laura, guided by a large language model dialogue. After the interaction, students completed surveys assessing their confidence, perceived authenticity, and cognitive engagement. Our research project has two primary aims: (1) How confident do students feel in making clinical decisions after interacting with an LLM-supported virtual patient?, and (2) How do user engagement (dialogue turns) and perceptions of the simulation relate to confidence gain in real patient interactions? Preliminary results reveal that the authenticity of the interaction significantly predicted students’ reported confidence gain. These findings suggest that virtual patient simulations can positively impact the users’ confidence– offering insight into the future integration of virtual educational tools in healthcare education.

Model-informed precision dosing (MIPD) utilizes pharmacokinetic/pharmacodynamic (PK/PD) models to optimize drug therapy. However, conventional MIPD often requires manual simulation and regimen selection, which are time-consuming and demand specialized expertise. Reinforcement learning (RL), in which an agent learns optimal decisions through iterative interactions with an environment, offers a scalable and automated alternative. In this study, we developed a model-informed Deep Q-Network (DQN) to personalize infliximab dosing for patients with Crohn's disease. The DQN was trained in a simulation environment incorporating a population PK model, inter-individual variability, and assay error. Virtual patients with randomly and independently sampled covariates from log-normal distributions were used to explore dosing strategies at Infusions 1, 3, and 4. Doses ranged from 1 to 10 mg/kg at Infusion 1 and from 1 to 20 mg/kg thereafter, with intervals of 4-12 weeks. The reward function prioritized achieving trough concentrations of 18-26 μg/mL before Infusion 3 and 5-10 μg/mL before Infusions 4 and 5, while penalizing overtreatment and additional infusions. The DQN policy converged after 80,000 episodes, yielding target attainment probabilities (PTAs) of 92.9% and 98.4% at Infusions 4 and 5, respectively, in 1000 virtual patients. High doses (11-20 mg/kg) were selected in only 0.2% of cases. At Infusion 4, 66.8% of patients received an 8-week interval, and 57.3% at Infusion 5. Retrospective real-world validation showed that patients whose actual doses matched DQN recommendations had trough levels significantly closer to target ranges. These findings support the feasibility of using DQN-based agents to enhance and automate infliximab individualized dosing in pediatric populations.

Introduction: Virtual reality (VR) immersive technologies are an emerging area in healthcare education involving a digital representation of a 3D environment and a head-set to “block out the real world” [1]. They allow for controlled, standardised and repeatable interactions [2] promoting equitable access to high-fidelity learning. Successful implementation necessitates collaboration with learners, to inform development of the product. The session aim was to create a platform for Occupational Therapy (OT) students to develop skills and knowledge with people experiencing suicidal ideation. This module was new for the OT programme. Methods: A half-day session was designed for 53 OT 2nd year prequalifying students at Brookes University Oxford. The VR module was entitled “The mental health practitioner” developed by Bodyswaps™, A preceding on-boarding session had been organised to familiarise students with the Bodyswaps™ platform. Students rotated in groups into a skills lab set up with the VR headsets but joined together in a classroom for a pre-briefing on ground rules, using VR headsets and psychological safety. Students interacted with a virtual patient experiencing suicidal thoughts, choosing responses to her statements and receiving feedback. The experience allowed for self-reflection, students assuming the role of the patient, listening to their own responses embodied by a chosen ‘avatar’. A debrief session was followed by a theory-based seminar on suicide. Results: Students completed an evaluation with Likert scales and free text questions. The session was overall well rated. 20/26 (77%) scored the session at least 7 out of 10. 65% indicated 7 or more out of 10 (10 being strongly agree) that the session helped them improve skills in relation to mental health practice. Some students preferred VR to live simulation while some felt it was artificial. There was a prominent theme around more time, privacy and space. Discussion: The session allowed students to practice difficult conversations in a low-risk immersive environment, through reviewing their own responses and appreciating the patient’s perspective, increasing preparedness for placement and future practice. Faculty staff could use the feedback to develop the module as an alternative to more familiar live-actor simulation. The debrief and theory session supported reflection and theoretical understanding. Feedback indicates a demand for more VR-based mental health training in the future and scope to develop this further for OT teaching. Ethics Statement: As the submitting author, I can confirm that all relevant ethical standards of research and dissemination have been met. Additionally, I can confirm that the necessary ethical approval has been obtained, where applicable.

Introduction: Increasingly adopted in healthcare education for their ability to engage learners, develop teamwork and critical thinking skills, escape rooms are defined as ‘live-action, team-based games where players discover clues, solve puzzles, and accomplish tasks in one or more rooms in order to accomplish a specific goal’ [1]. The literature suggests that escape rooms have the potential to engage learners[2,3]. Our simulation team, consisting of academic and simulation technicians, created a virtual escape room using IntuifaceTM software for our immersive learning environment. This interactive touchscreen experience allowed nursing students to practice critical thinking, communication, and teamwork as they navigated a virtual patient’s home, consisting of a linear storyline of puzzles within a 40-minute limit. These were focused on wound assessment and management. Methods: This learning experience involved groups of up to 12 students, structured with rotating participation of 5-6 active learners, and 5-6 active observers who contributed suggestions and insights. Solving the sequential puzzles demanded effective teamwork, clear communication, and the application of knowledge relevant to the scenario’s phases: history taking, information gathering, the correct utilization of wound assessment tools, and the selection of appropriate wound dressings. A simulation technician facilitated the technical operation, while an academic facilitator guided the in-experience discussions and debriefing to enhance learning. Results: Feedback was collected from participants at the end of each session via a QR code. A total of 6 sessions were delivered in 1 day. 65 students took part, with 31 completing the evaluation form (response rate of 47.7%). Overall, student feedback indicated a positive learning experience and participants reported high levels of enjoyment and engagement with the activities, with evidence of perceptions of successful teamwork and communication. Additionally, at least one student noted the development of valuable skills such as critical thinking and problem-solving. The unique and fun nature of the session appears to have contributed to this learning opportunity, see Figure 1. Discussion: The feedback identified a potential area for improvement which was highlighted by “more time and guidance”, suggesting that some students may have felt pressed for time, or required additional support to fully benefit from the activities. This warrants consideration in future session planning to ensure adequate time allocation and appropriate levels of guidance are provided to accommodate all learners. Despite this point for potential enhancement, the feedback suggests the learning experience was innovative and well-received by the participating students, and will be embedded in future nursing curricula. Ethics Statement: As the submitting author, I can confirm that all relevant ethical standards of research and dissemination have been met. Additionally, I can confirm that the necessary ethical approval has been obtained, where applicable.

In patients with Mycobacterium abscessus (MAB) lung disease (LD), guideline-based therapy (GBT) achieves sputum culture conversion in only 20-35% of patients. The poor efficacy is reflected in the hollow fibre model system for MAB-LD (HFS-MAB), where GBT drugs' maximal microbial kill (Emax) below day 0 burden (B0) were amikacin 3 colony-forming units (cfu)/mL, clarithromycin-cefoxitin-amikacin (GBT) 17 cfu/mL, imipenem 20 cfu/mL, and tigecycline 24 cfu/mL. We tested the bacterial leucyl-tRNA synthetase (LeuRS) inhibitor, epetraborole, for MICs in 59 MAB isolates, and with seven epetraborole exposures in the HFS-MAB over 21 days. Data were analysed using the inhibitory sigmoid Emax model for microbial kill, and a quadratic function for resistance. Monte Carlo experiments (MCE) were performed to identify the optimal epetraborole dose for the clinic. The epetraborole MIC50 was 0.125 mg/L, and the MIC90 was 0.25 mg/L. Epetraborole killed 253 cfu/mL below B0. However, epetraborole microbial effect was terminated by antimicrobial resistance, in an exposure-dependent fashion. Whole genome sequencing revealed concentration-dependent generation of resistance-associated LeuRS mutations, including novel mutations such as Asp433Val, Arg324Ser, and Phe310Cys. The exposure mediating 80% of Emax (EC80) was an AUC0-24/MIC = 202.73. In MCE 750 mg twice daily achieved EC80 in 95.6% and 79.2% of virtual patients for intravenous versus oral administration, respectively. Epetraborole microbial kill is the best, thus far, achieved in the HFS-MAB, more than two times omadacycline's 123 cfu/mL below B0. We propose to test a novel combination regimen of epetraborole plus omadacycline plus a β-lactam versus GBT first in the HFS-MAB, followed by clinical trials.

Objective: To explore medical students’ perceptions towards the integration of basic science into virtual patient (VP) cases and to evaluate the impact on self-perceived clinical reasoning (CR) ability. Methods: We conducted a qualitative and explorative study involving 14 medical students during their clinical placement within rheumatology. Basic science elements were integrated into five VP scenarios using the virtual interactive case simulator. Students’ perceptions were explored through the analysis of semi-structured interviews with seven students, followed by Malterud’s systematic text condensation. Results: The analysis yielded five themes: (i) appreciation of basic science knowledge, (ii) ambiguity towards basic science as an obstacle for integration, (iii) the effect of integration on self-perceived clinical reasoning, (iv) engaging design of the basic science integration, and (v) low knowledge of clinical reasoning concepts. Despite positive perceptions, students demonstrated low motivation for independent engagement in practice. The students who completed the intervention reported enhanced self-perceived CR abilities, as evidenced by more comprehensive thinking processes. Conclusions: Basic science integration into VP cases was positively perceived and appeared to enhance self-perceived CR abilities. However, students’ reluctance to independently engage posed implementation challenges. Early introduction of CR learning objectives and CR as a conceptual framework may improve motivation and provide coherence for the integration of basic science in the clinical components of medical education.

Optimizing high-dose methotrexate treatment regimens to improve exposure and mitigate toxicity in infant patients with acute lymphoblastic leukemia

With the continuous development of digital technology, three-dimensional (3D) facial scanning technology is becoming more and more widely used in dentistry, where it enhances the precision of various processes of clinical diagnosis and treatment, and provides new perspectives and tools for dental research and application. Nowadays, 3D facial scanning technology meets clinical needs in terms of accuracy, and most facial scanners have an accuracy of 140-1 330 μm, and the 3D facial scanning related applications of smartphones can also meet the clinical application needs to a certain extent. In the clinical field, the 3D scanner can be combined with a variety of technologies to achieve multi-modal data fusion and create virtual patients, and its prediction rate in obstructive sleep apnea hypopnea syndrome is as high as 91%, which has great clinical application value. This article aims to explore the research status, clinical applications, and accuracy analysis of 3D facial scanning techniques in the field of dentistry.