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However, results from studies focusing on VR-based stroke rehabilitation remain inconsistent, largely due to the use of noncustomized interventions in previous trials.\n<strong>Objective:<\/strong> To enhance rehabilitation services and inform the development of patient-centered VR rehabilitation systems, this study aimed to (1) explore the experiences and unmet needs of survivors of stroke during current hospital rehabilitation, and (2) examine their perspectives on the use of VR technology in poststroke rehabilitation.\n<strong>Methods:<\/strong> We conducted a qualitative thematic analysis based on descriptive phenomenology between January and July 2025 at the China Rehabilitation Research Center. Adult patients with a clinical diagnosis of stroke within the past 18 months were eligible. A total of 21 survivors of stroke (mean age, 52.7, SD 17.3 y; men, n=17) were included. Data were collected through face-to-face semistructured interviews, complemented by a short questionnaire on sociodemographic, clinical, and technology-use characteristics. All interviews were audio-recorded, transcribed verbatim, and analyzed using a thematic approach, with thematic saturation used to determine the sample size.\n<strong>Results:<\/strong> After a stroke, patients experience significant physical and psychological changes. On the one hand, the sudden loss of abilities alters their perceived roles within the family and society; on the other hand, the sharp contrast between their desire for recovery and their current recovery limitations creates substantial psychological pressure. Accepting their condition and rebuilding confidence is a long-term process. Traditional rehabilitation is commonly described as burdensome, monotonous, and lacking continuity after discharge. Although patients desire a better rehabilitation approach and improved outcomes, attitudes toward VR-based rehabilitation vary. Some view VR as a convenient tool, while others express no interest or perceived need for technology-based rehabilitation. Patients indicated that serious games should be diversified to meet different individual and training needs, and should incorporate clearer feedback mechanisms, appropriate scoring systems, adjustable difficulty levels, and progressive game chapters. Functional expectations for VR systems included family involvement, access to personal rehabilitation data, telerehabilitation support, safety monitoring, and technical support.\n<strong>Conclusions:<\/strong> Stroke rehabilitation services in China require improvement in the appeal of rehabilitation content, patient self-management, and continuity of care. Although patients desire better rehabilitation approaches and outcomes, the effective integration of VR technology must account for factors, such as personal characteristics and preferences, as well as socioeconomic status. Unlike previous studies that primarily examined user experiences with digital technologies or compared rehabilitation outcomes, our research contributes to the literature by linking the challenges and patient needs in conventional rehabilitation with concrete directions for the future design of VR rehabilitation. These insights deepen current understanding of how VR technologies can be meaningfully integrated into stroke care and provide a roadmap for developing patient-centered and culturally responsive VR solutions.\n","subjectClass":"stroke patients;rehabilitation;digital health technology;virtual reality;qualitative study;user-requirements;patient-centered design","language":"en","dateSubmitted":"2025-09-21 06:53:24","dateStatusModified":"2026-02-02 12:00:11","lastModified":"2026-02-02 12:00:11","status":"3","granularStatusId":"022_010","submissionProgress":"0","currentRound":"2","submissionFileId":"1251770","revisedFileId":"1340491","reviewFileId":"1251800","copyeditFileId":"1347291","conflictOfInterest":"None declared.","contributors":"","suppFiles":[{"_data":{"title":"License to Publish Form (HelloSign)","fileId":"1341634","creator":null,"subject":null,"suppFileId":"727265","articleId":"84532","publicSuppFileId":null,"type":"License to publish form (upload after 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While SBL has demonstrated its value in education, many educators find the process of developing new, unique scenarios to be time-intensive, creating limits to the variety of issues students may experience within educational settings. Generative artificial intelligence (AI) platforms, such as ChatGPT (OpenAI), have emerged as a potential tool for developing simulation case studies more efficiently, though little is known about the performance of AI in generating high-quality case studies for interprofessional education.\n<strong>Objective:<\/strong> This study aimed to generate geriatric case scenarios across 5 AI platforms by a transdisciplinary team and systematically evaluate them for quality, accuracy, and bias.\n<strong>Methods:<\/strong> Ten geriatric case studies were generated using the same prompt from 5 different generative AI platforms (N=50): ChatGPT, Claude (Anthropic AI), Copilot (Microsoft), Gemini (Google), and Grok (xAI). An evaluation tool was developed to collect evaluative data to assess the content and quality of each case, sociodemographic data of the featured patient, the appropriateness of each case for interprofessional education, and potential bias. Case quality was evaluated using the Simulation Scenario Evaluation Tool (SSET)<i>.<\/i> Each case was evaluated by 3 team members who had experience in SBL education. Assessment scores were averaged, and qualitative responses were extracted to triangulate patterns found in the quantitative data.\n<strong>Results:<\/strong> While each AI platform was able to generate 10 unique case studies, the quality of studies varied within and across platforms. Generally, evaluators felt that the content in the cases was accurate, though some cases were not realistic. Some patient populations and common conditions among older adults were underrepresented or absent across the cases. All cases were set within traditional health care settings (eg, hospitals and routine medical visits). No cases featured home-based care. Based on the average SSET scores, reviewers assessed ChatGPT to be the highest overall performer (mean 3.27, SD 0.45, 95% CI 2.95-3.59) while Grok received the lowest scores (mean 1.61, SD 1.26, 95% CI 0.71-2.51). Platforms performed best at generating learning objectives (mean 3.35, SD 1.08, 95% CI 3.04-3.65) and lowest on their ability to describe supplies and materials that may be available in hypothetical scenarios (mean 1.27, SD 0.84, 95% CI 1.03-1.51).\n<strong>Conclusions:<\/strong> This study is the first to systematically evaluate and compare multiple generative AI platforms for case study generation using a validated assessment tool (SSET) and provides evidence-based guidance on selecting and using AI tools effectively. The findings offer practical direction for educators navigating available generative AI tools to enhance training for health care professionals, including specific strategies for prompt engineering that can improve the quality of SBL resources in interprofessional education. These insights enable educators to leverage AI capabilities while maintaining pedagogical rigor.\n","subjectClass":"chatbots;generative artificial intelligence;geriatric nursing;interprofessional education;patient simulations","language":"en","commentsToEditor":"","dateSubmitted":"2025-08-27 14:47:13","dateStatusModified":"2026-01-30 15:00:35","lastModified":"2026-01-30 15:00:35","status":"3","granularStatusId":"022_010","submissionProgress":"0","currentRound":"2","submissionFileId":"1233553","revisedFileId":"1332324","reviewFileId":"1233597","copyeditFileId":"1338490","conflictOfInterest":"None declared.","contributors":"","suppFiles":[{"_data":{"title":"License to Publish Form (HelloSign)","fileId":"1332492","creator":null,"subject":null,"suppFileId":"720831","articleId":"83085","publicSuppFileId":null,"type":"License to publish form (upload after 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To ensure a similar level of care to in-person consultations, high-quality images are essential.\n<strong>Objective:<\/strong> The aim of this study is to develop an image quality analysis tool to assess patient- and primary care physician (PCP)\u2013derived images using a deep learning model leveraging multiple instance learning and ordinal regression for model predictions.\n<strong>Methods:<\/strong> The data set used for this study was acquired from patient-derived images submitted to the Department of Dermatology, Duke University, between August 21, 2018, and December 31, 2019, and PCP-derived images between March 1, 2021, and June 30, 2022. Seven dermatology faculty members with a designation of professor, associate professor, and assistant professor evaluated 400 images each, and 2 dermatology residents evaluated 400 images, assuring that each image had 4 different quality labels. We used a pretrained model VGG16 architecture, further fine-tuned by updating weights based on the input data. The images were taken with cell phones (patients) or cameras (PCPs) in RGB scale, with the resolution being 76 pixels per inch for both height and width, and the average pixel size of the image being 2840\u00d72793 (SD 986\u00d7983; 1471 inch<sup>2<\/sup>, SD 707 inch<sup>2<\/sup>). The optimal threshold was determined using the Youden index, which represents the best trade-off between sensitivity and specificity and balance the number of true positives and true negatives in the classification results. Once the model predicts the rank, the ordinal labels are transformed to binary labels by using a majority vote as the goal is to distinguish between 2 distinct categories (good vs bad quality) and not predict quality as a continuous variable.\n<strong>Results:<\/strong> Based on the Youden index, we achieved a positive predicted value of 0.906, implying that the model will predict 90% of the good-quality images as such, while 10% of the poor-quality images are predicted as being of good quality to enhance clinical utility, with an area under the receiver operating characteristic curve (AUC) for the test set at 0.885 (95% CI 0.838-0.933) and sensitivity, specificity, and negative predictive value (NPV) of 0.829, 0.784, and 0.645, respectively. Further evaluation on independent validation consisting of 300 images from patients and 150 images from PCPs revealed AUCs of 0.864 (95% CI 0.818-0.909) and 0.902 (95% CI 0.85-0.95), respectively. The sensitivity, specificity, positive predicted value, and NPV for the 300 images were 0.827, 0.800, 0.959, and 0.450, respectively.\n<strong>Conclusions:<\/strong> This study shows a practical approach to improve image quality for clinical decision-making. While patients and PCPs may have to capture additional images (due to lower NPV), this is offset by the reduced workload and improved efficiency of clinical teams due to the receipt of higher-quality images. Additional images can also be useful if all images (good or poor) are transmitted to medical records. 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