Clinical Research Directory

Generative AI-Based Immersive Training for Endodontic Risk Disclosure and Informed Consent Among Dental Students

The objective of this prospective, parallel-group, 1:1 randomized controlled trial is to evaluate the effectiveness of a generative artificial intelligence (AI)-based immersive training program, utilizing the Gemini platform, in improving endodontic risk disclosure and informed consent (RD-IC) quality, communication competence, self-efficacy, and reducing communication anxiety among undergraduate dental students during their clinical clerkship. Traditional pedagogical methods in dental education, such as case-based learning (CBL) and peer role-playing, often struggle to simulate the high-stakes interpersonal tension and technical complexity involved in specialized endodontic risk disclosure. Complications like instrument separation, pulp floor perforation, and acute flare-ups require clear, empathetic, and legally sound communication. While standardized patients (SPs) are effective, they pose severe logistical and financial constraints for large student cohorts. To address this gap, 96 fifth-year undergraduate dental students undergoing clinical rotation were recruited and randomly allocated to either the experimental group (1-month generative AI-based immersive training via the Gemini platform) or the control group (1-month conventional case-based learning). The AI intervention provided interactive, real-time conversational simulations across six high-stakes endodontic clinical scenarios, delivering instantaneous, personalized feedback on technical accuracy and empathetic delivery. The primary outcome evaluated was the RD-IC completeness score. Secondary outcomes included SEGUE communication framework scores, Self-Efficacy in Clinical Communication Scale (SCS) scores, Communication Anxiety Scale (CAS) scores, blinded standardized patient (SP) ratings, user satisfaction, and long-term clinical skill retention assessed at 1-month (T2-1m) and 2-month (T2-2m) post-intervention follow-ups.

Adaptive Real-Time Voice Virtual Patients for Clinical Communication Training

This study evaluated whether an emotionally and interactionally adaptive real-time voice virtual patient can improve clinical communication training for resident physicians. Communication with patients in emotionally challenging situations, such as breaking bad news, requires clinicians to deliver information clearly, recognize emotional cues, respond empathically, and maintain a coherent interaction. Standardized patient training can support these skills but is resource intensive and difficult to provide repeatedly. Real-time voice virtual patients may offer scalable practice opportunities, but simple spoken interaction alone may not provide patient responses that change meaningfully according to the learner's communication. The adaptive virtual patient used in this study was designed to update the patient's emotional tone, openness, defensiveness, and interactional style during the conversation based on the learner's communication behavior. The study compared this adaptive virtual patient training with a non-adaptive real-time voice virtual patient and with standardized patient training. Resident physicians were randomly assigned in a 1:1:1 ratio to one of three groups: emotionally and interactionally adaptive virtual patient training, non-adaptive virtual patient training, or standardized patient training. Participants completed two communication training sessions focused on breaking bad news. After training, participants completed learner-reported measures of perceived response contingency and patient realism. Approximately four weeks later, participants completed a mini-objective structured clinical examination using a human standardized patient to assess communication performance in a delayed near-transfer setting. The primary outcome was the total mini-OSCE communication score at four weeks. Secondary outcomes included mini-OSCE domain scores for structured delivery, emotional responsiveness, and interactional process; learner-reported response contingency and patient realism; and expert-rated behavioral consistency and clinical credibility of virtual patient interactions.

Smartphone Use in Healthcare Settings Among Nurses

This study assessed patterns of smartphone use among nurses and evaluated its impact on clinical performance and patient safety in healthcare settings in Egypt, using a convergent mixed-methods design. A cross-sectional survey was conducted between October 2025 and February 2026 across diverse clinical settings at Alexandria University Hospitals, including critical and emergency care, medical-surgical wards, operating theaters, outpatient clinics, home care services, and psychiatric support units. Of 900 nurses invited, 368 completed the online questionnaire via Google Forms (response rate: 40.9%). Quantitative data assessed nurses' patterns of smartphone use and its impact on patient care using an adapted validated scale. Qualitative data were collected through two open-ended narrative questions exploring smartphone-related clinical incidents and recommendations for safe use. A total of 105 clinical incidents attributed to smartphone distraction were identified and categorized into four themes, while 176 respondents provided actionable recommendations categorized into five strategic pillars for safe clinical integration.