Raniah N. Aldekhyyel

Background/Objectives: Wearable devices, such as smartwatches, represent an emerging digital health tool that can enhance patient engagement, support patient-provider communication, and enable more personalized, patient-centered care. Understanding public willingness to share health-generated data with healthcare providers is essential for informing digital health implementation strategies that prioritize patient trust and active participation in the care process. This study aimed to address this gap by assessing the willingness of a sample of smartwatch users in Riyadh to share their health-generated data and identifying factors associated with this willingness. Methods: A cross-sectional study was conducted using venue-based convenience sampling at a large public venue in Riyadh, Saudi Arabia. Data were collected using an electronic self-administered questionnaire. Descriptive statistics summarized participant characteristics, while bivariate analyses and multivariable logistic regression were used to examine factors associated with willingness to share health-generated data. Results: Among the 391 participants, 168 reported owning a smartwatch and comprised the population for the primary analysis of willingness to share health-generated data. Among smartwatch owners (n = 168), 104 (62%; 95% CI: 54–69%) expressed willingness to share their health-generated data. Among those willing to share, healthcare providers were the most preferred recipients (95%, 99/104), followed by family members and friends (63%, 66/104). Most demographic, socioeconomic, health-related, and behavioral characteristics were not significantly associated with willingness to share; however, in bivariate analysis, previous use of online support groups was significantly associated with greater willingness to share health-generated data (OR 5.00; 95% CI: 1.66–15.10; p = 0.002). None of the predictors included in the multivariable model was independently associated with willingness to share. Conclusions: Most smartwatch users were willing to share their health-generated data with healthcare providers, reflecting positive public attitudes toward integrating this data into healthcare systems. These findings support Saudi Arabia’s digital health transformation initiatives, while highlighting the need to address privacy, trust, and data governance to enable successful implementation.

Background/Objectives: Recent advancements in digital health have facilitated the expansion of mobile health (mHealth) apps. This study examines the top-ranked Arabic wellness mHealth apps in the Middle East and North Africa (MENA) region and evaluates wellness apps in Saudi Arabia for their potential to promote health-related behavior change using the App Behavior Change Scale (ABACUS). Methods: A cross-sectional review was conducted using a systematic three-phase approach to identify and evaluate Arabic wellness apps, with the dataset extracted from the Sensor Tower platform. In Phase 1, apps were identified based on predefined country, language, and wellness criteria. In Phase 2, app descriptions were screened for behavior change features. In Phase 3, eligible apps available in Saudi Arabia were downloaded and evaluated using the ABACUS. Results: Egypt had the highest number of identified Arabic wellness apps (n = 9), followed by Iraq and the United Arab Emirates (UAE) (n = 8 each), while Yemen had the fewest identified apps. For potential behavior change apps, Tunisia and the UAE had the highest apps (n = 7), whereas Yemen and Libya had the lowest (n = 1 each). All Arabic wellness apps identified in Saudi Arabia were classified as promoting physical activity with only one app encouraging healthy eating. The total ABACUS scores were relatively consistent, ranging from 15 to 17 out of 21. Conclusions: Arabic mHealth wellness apps identified across the included MENA countries were limited in number, while apps available in Saudi Arabia showed variability in the incorporation of behavior change features.

Background: Generative artificial intelligence (AI) systems are increasingly used to produce medical illustrations for education; however, their anatomical accuracy in complex domains such as congenital heart disease (CHD) remains insufficiently validated. Methods: In an assessor-blinded comparative study, we evaluated AI-generated CHD illustrations from two contemporary text-to-image platforms (ChatGPT-5/ChatGPT-Images and Gemini NanoBanana) against human-modified educational images. Twenty different CHD types were included, yielding 147 images that were assessed by 20 physicians (10 CHD experts and 10 non-specialists). Images were rated across four domains: anatomical accuracy, label usefulness, visual attractiveness, and suitability for medical education (total score range, 4–12). Results: Among 2,940 total image evaluations, the human-modified images demonstrated the highest anatomical accuracy (48.3% rated accurate), followed by NanoBanana (22.7%), while ChatGPT-generated images were predominantly rated as fabricated or incorrect (86.3% for ChatGPT-5 and 85.2% for ChatGPT-Images; p<0.001). Educational usability “as is” was highest for the human-modified images (37.9%) compared with NanoBanana (13.1%) and ChatGPT platforms (≤2.1%; p<0.001). Median overall quality scores were 8 for the human-modified CHD images and NanoBanana, versus 4 for both ChatGPT systems (p<0.001). In multivariable analysis, NanoBanana images were the closest to the human-modified images in quality (95% CI, 0.91–0.98), while ChatGPT-Images (95% CI, 0.58–0.63) and ChatGPT-5 (95% CI, 0.55–0.59) showed marked quality reductions. Conclusions: The current generative AI systems produced visually compelling but frequently anatomically inaccurate CHD illustrations, falling substantially short of the current educational standards. Model choice strongly influences performance, with Gemini NanoBanana outperforming ChatGPT-based systems yet remaining inferior to expert-designed human-modified images. AI-generated cardiac imagery should be used only within expert-reviewed educational workflows rather than as independent instructional resources.

Raniah N. Aldekhyyel College of Medicine, King Saud University, Saudi Arabia (raldekhyyel@ksu.edu.sa) Sharifa Abdullah Aldosarry College of Pub...

Objective: With rising digital tool use and lack of standard evaluation criteria in medical education, students should learn to assess the quality of medical apps. We designed a workshop to equip medical students with app evaluation skills. This study describes the workshop and experiences of students and faculty with the Mobile App Rubric for Learning (MARuL) instrument. Methods: The study used qualitative design and consecutive sampling, including all third-year medical students enrolled in a medical informatics course. Faculty facilitators introduced students to the MARuL instrument and trained them on its use. Students, grouped based on their familiarity with medical apps, conducted evaluations, and feedback was collected through an online form. The data were analyzed using thematic content analysis. Faculty also reflected on the workshop’s implementation and the effectiveness of the instrument. Results: A total of 275 students participated, identifying challenges such as the instrument’s length, complex terminology, scoring difficulties, limited answer options, and the lack of open-ended questions. Faculty reported difficulties in explaining the MARuL and conducting the workshop effectively. Conclusion: The findings suggest the importance of integrating standard medical apps evaluation tools into the medical curriculum. These tools should be time-efficient, easy to understand, and simple to interpret for their utilization in an educational setting.

We aimed to assess medical students’ use of decision-support medical apps and evaluate their perception of app use. A cross-sectional multi-center observational study was conducted among medical students with and without a medical informatics course as part of their undergraduate medical curriculum. We assessed trust, perceptions, patient impression, reliability, and comfort using an online survey. A total of 439 responses were received. There were significant differences between the two groups when indicating which apps, they trust. Students agreed that using apps enhanced knowledge (91%), saved time (88%), improved patient care (85%), and increased diagnostic accuracy (82%). Students indicated that patients would think that students didn’t know what they were doing (63%) or students were fresh out of training (53%) when using apps in the presence of patients. Incorporating medical app usage as part of learning may increase trust and comfort with using medical apps in medical practice.