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Background and Aim: The present study aimed to evaluate the anxiety, depression, and fear in people with and without diabetes during the transmission of the SARS-CoV-2 Omicron variant. Methods: In this comparative study (n= 353), people with diabetes (n= 121) and those without diabetes (n= 232) were investigated for physiological aspects of COVID-19 during the SARS-CoV-2 Omicron variant outbreak. This study was performed at the Prince Sultan Military Medical City (PSMMC), Riyadh, Saudi Arabia. On request, the participants responded to the survey questionnaires, providing data on demographics, Generalized Anxiety Disorder (GAD-7), and Patient Health Questionnaire-2 (PHQ-2) to screen for anxiety, depression, and COVID-19-related fears. Results: The GAD statement revealed a significantly higher degree of nervousness and anxiety among people with diabetes when compared to people without diabetes (P = 0.001). For the other GAD statements, no statistical difference was observed between the responses of people with and without diabetes, including the overall GAD-7 (P = 0.091). The PHQ-2 statement, concerning a feeling of depression or hopelessness noted a significant escalation among diabetes compared to nondiabetes (P = 0.004). Furthermore, in the overall PHQ-2, a significant escalation (P = 0.011) was noticed among people with diabetes compared to nondiabetes. In addition, people with diabetes had a significantly high (P = 0.037) COVID-19-related fear reported, compared to people without diabetes. Conclusion: The need for comprehensive research is urgent, as it will facilitate a better understanding of the physiological aspects of COVID-19 for patients with diabetes.

Introduction: Within the near future, doctors can be anticipated to encounter patients in very distinctive wellbeing care settings compared with the present time. As a result, artificial intelligence will be an essential tool. Objectives: The purpose of this study is to investigate the attitudes of Jordanian medical students regarding Artificial Intelligence (AI) and Machine Learning (ML). Moreover, to estimate the level of knowledge and understanding of the effects of AI on medical students. Methods: Nine hundred medical students from six universities in Jordan participated in this survey. The participants were asked to fill out an electronic pre-validated questionnaire using Google’s forms and those forms were published via social media. The questionnaire included questions of Likert and dichotomous questions. Results: 89% of the students believed in the importance of AI in the medical field, and 71.4% believed in the beneficiary of teaching AI in the medical career. 47% of the students had an understanding of the basic principles of AI, 68.4% of the students believed that it is mandatory for medical students to receive knowledge of AI. Statistically, students who received teaching/training in AI were more likely to consider radiology as a career given the advancement in AI (p = 0.000). Conclusion: Medical students in Jordanian universities appreciate the importance of artificial intelligence and machine learning in medical advancements. Adding courses and training related to artificial intelligence and machine learning to the study plan should be considered.