The Chatbot Generative Pre-Trained Transformer (ChatGPT) has garnered great attention from the public, academicians and science communities. It responds with appropriate and articulate answers and explanations across various disciplines. For the use of ChatGPT in education, research and healthcare, different perspectives exist with some level of ambiguity around its acceptability and ideal uses. However, the literature is acutely lacking in establishing a link to assess the intellectual levels of ChatGPT in the medical sciences. Therefore, the present study aimed to investigate the knowledge level of ChatGPT in medical education both in basic and clinical medical sciences, multiple-choice question (MCQs) examination-based performance and its impact on the medical examination system. In this study, initially, a subject-wise question bank was established with a pool of multiple-choice questions (MCQs) from various medical textbooks and university examination pools. The research team members carefully reviewed the MCQ contents and ensured that the MCQs were relevant to the subject’s contents. Each question was scenario-based with four sub-stems and had a single correct answer. In this study, 100 MCQs in various disciplines, including basic medical sciences (50 MCQs) and clinical medical sciences (50 MCQs), were randomly selected from the MCQ bank. The MCQs were manually entered one by one, and a fresh ChatGPT session was started for each entry to avoid memory retention bias. The task was given to ChatGPT to assess the response and knowledge level of ChatGPT. The first response obtained was taken as the final response. Based on a pre-determined answer key, scoring was made on a scale of 0 to 1, with zero representing incorrect and one representing the correct answer. The results revealed that out of 100 MCQs in various disciplines of basic and clinical medical sciences, ChatGPT attempted all the MCQs and obtained 37/50 (74%) marks in basic medical sciences and 35/50 (70%) marks in clinical medical sciences, with an overall score of 72/100 (72%) in both basic and clinical medical sciences. It is concluded that ChatGPT obtained a satisfactory score in both basic and clinical medical sciences subjects and demonstrated a degree of understanding and explanation. This study’s findings suggest that ChatGPT may be able to assist medical students and faculty in medical education settings since it has potential as an innovation in the framework of medical sciences and education.

Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressure wounds are becoming increasingly common globally. These conditions can result in highly debilitating repercussions for those affected, with limb amputations and increased mortality risk resulting from infection becoming more common. New methods to assist clinicians in chronic wound care are therefore vital to maintain high quality care standards. This paper presents an improved HarDNet segmentation architecture which integrates a contrast-eliminating component in the initial layers of the network to enhance feature learning. We also utilise a multi-colour space tensor merging process and adjust the harmonic shape of the convolution blocks to facilitate these additional features. We train our proposed model using wound images from light skinned patients and test the model on two test sets (one set with ground truth, and one without) comprising only darker skinned cases. Subjective ratings are obtained from clinical wound experts with intraclass correlation coefficient used to determine inter-rater reliability. For the dark skin tone test set with ground truth, when comparing the baseline results (DSC=0.6389, IoU=0.5350) with the results for the proposed model (DSC=0.7610, IoU=0.6620) we demonstrate improvements in terms of Dice similarity coefficient (+0.1221) and intersection over union (+0.1270). Measures from the qualitative analysis also indicate improvements in terms of high expert ratings, with improvements of >3% demonstrated when comparing the baseline model with the proposed model. This paper presents the first study to focus on darker skin tones for chronic wound segmentation using models trained only on wound images exhibiting lighter skin. Diabetes is highly prevalent in countries where patients have darker skin tones, highlighting the need for a greater focus on such cases. Additionally, we conduct the largest qualitative study to date for chronic wound segmentation. All source code for this study is available at: https://github.com/mmu-dermatology-research/hardnet-cws.

Background: Type 1 diabetes (T1D) is a growing global health concern, with a notable rise in incidence in Saudi Arabia. Despite the potential benefits of early detection through screening programs, such initiatives are currently lacking in Saudi Arabia and other Arab countries. Objective: This study aims to evaluate the feasibility, acceptability, and cost-effectiveness of a T1D-screening program targeting high-risk individuals, specifically children with a first-degree relative diagnosed with T1D. Methods: The VISION-T1D program is a prospective cohort study focused on the early detection of presymptomatic T1D by screening children aged 2-18 years. The primary screening method involves testing for islet autoantibodies, including insulin autoantibodies, glutamic acid decarboxylase autoantibodies, insulinoma associated-2 autoantibodies, and zinc transporter-8 autoantibodies. Optional genetic testing, including human leukocyte antigen phenotyping and the genetic risk score, is offered. Outcomes include the feasibility of the screening process, prevalence of early-stage T1D, psychological impacts, educational intervention effectiveness, progression rates to stage-3 T1D, and economic viability. Results: The VISION-T1D program began in May 2024. As of December 2024, a total of 176 families have been enrolled. Data collection will continue until April 2025, with final data analysis projected for mid-2025. Conclusions: The VISION-T1D study provides a practical approach to T1D screening tailored to the health care landscape of Saudi Arabia. The insights gained from this pilot program will inform the development of a national, population-based screening initiative designed to reduce diabetic ketoacidosis at diagnosis, improve long-term outcomes, and alleviate the economic burden of T1D. The VISION-T1D initiative could also serve as a scalable and sustainable model that can be adopted internationally, contributing to global efforts to manage and prevent T1D. Trial Registration: ClinicalTrials.gov NCT06513247; https://clinicaltrials.gov/study/NCT06513247 International Registered Report Identifier (IRRID): DERR1-10.2196/70575

Aims: The study was aimed at assessing the role of the MiniMed780G system of glycemic control before, during, and after Ramadan among people with Type 1 diabetes (PwT1D). Methods: This is a single‐center retrospective analysis of MiniMed780G system users aged 14 years and above whose glycemic profiles were collected from February 21 to May 20, 2023, which corresponds to the Hijri months of Sha’ban, Ramadan, and Shawwal 1444/1445. Data was collected, processed, and analyzed in the framework of the Medtronic Galaxy service of the One Hospital Clinical Service (OHCS) program in Dallah Hospital, Riyadh, Saudi Arabia. Data from 43 PwT1D (24 females, mean age 30 ± 11 years with 14 ± 8 years from diabetes onset) using the MiniMed780G system were collected. Results: Overall, the 3‐month (Sha’ban, Ramadan, and Shawwal) mean sensor glucose (SG), time in range (TIR) (70–180 mg/dL), time below range (TBR) (54–69 mg/dL and < 54 mg/dL), time above range (TAR) (180–250 mg/dL and > 250 mg/dL), and glucose management indicator (GMI) showed no statistical differences within the three periods. No differences in insulin total daily dose have been detected, and no diabetic ketoacidosis (DKA) or severe hypoglycemia events occurred. Conclusion: The use of the MiniMed780G system is safe with favorable glycemic outcomes across nonfasting and fasting months.

Objectives: The objective of this manuscript is to translate, adapt, and validate an Arabic version of the Diabetes Eating Problem Survey—Revised (DEPS-R) questionnaire to assess disordered eating behaviors (DEBs) in adolescents with T1D in Saudi Arabia. Additionally, the study sought to estimate the prevalence of DEBs and analyze its associations with glycemic control and diabetes-related complications. Methods: A cross-cultural validation study was conducted following the COSMIN guidelines. The DEPS-R questionnaire was translated into Arabic through forward and backward translation involving expert panels, including psychiatrists, diabetologists, and linguists. A sample of 409 people with type 1 diabetes (PwT1D) (58.4% females) aged 12–20 years was recruited from outpatient diabetes clinics in the five main regions of Saudi Arabia. Participants completed the Arabic DEPS-R and the validated Arabic version of the SCOFF questionnaire. Sociodemographic, anthropometric, and biochemical data were collected, and statistical analyses, including confirmatory factor analysis (CFA) and internal consistency tests, were conducted. Results: The Arabic DEPS-R exhibits strong internal consistency (Cronbach’s alpha = 0.829) and high test–retest reliability (ICC = 0.861), with a CFA supporting a three-factor structure, namely body weight perception, disordered eating behaviors (DEBs), and bulimic tendencies. Notably, higher DEPS-R scores are significantly linked to elevated HbA1c levels, increased BMI, and more frequent insulin use. Alarmingly, 52.8% of participants show high-risk DEB, which is directly associated with poor glycemic control (HbA1c ≥ 8.1%) and a heightened risk of diabetic ketoacidosis (DKA). Conclusions: The Arabic DEPS-R is a valid and reliable tool for screening DEBs among Saudi adolescents with T1D. Findings underscore the necessity for early identification and intervention to mitigate the impact of EDs on diabetes management and overall health outcomes.