Background: Artificial intelligence (AI) is an advanced computer technology used in the medical field to elude the errors and enhance effectiveness and efficiency, especially in clinical work. Developing countries can utilize the same models to improve their health care system as the industrialized world. Globally medicine is evolving to the era of "Artificial intelligence", medical education must include these modern technologies and competencies to reform. We intended to determine the attitude of the medical students towards the introduction of AI in Undergraduate Medical Education in District Peshawar. Methods: This cross-sectional descriptive study was carried out among 384 students of two medical colleges in Peshawar, utilizing a convenient sampling technique for data collection. A self-administered questionnaire, with 5 points Likert scale was used to collect data. Data was analyzed through SPSS version (22.2). Results: Majority of the students 61.7% had no previous knowledge of AI. Mean scores for AIs perceived usefulness in Radiology, replacement with human Radiologist, anticipated dominance in clinical practice, willingness for introduction in medical education, excitement to adopt, perceived as a burden, practicability were 1.89, 2.83, 2.76, 2.35, 2.13, 3.18, 2.39 respectively. Conclusion: The positive attitude was seen among medical students regarding the inclusion of Artificial intelligence in undergraduate medical education.

In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset.

Background: Medical students have a very extensive curriculum and a demanding time period during their undergraduate studies (1-5 years). Research has shown that medical students experience a high level of stress affecting their social, emotional as well as mental health. According to many studies formal mentoring is considered as a key to overcome these problems. Currently Khyber Pakhtunkhwa is deficient in formal mentorship program for medical education, the purpose of this article was to determine the presence of the programme and to establish the basis for formal mentoring with in medical colleges. Methods: The study was a descriptive cross-sectional study. A total of 300 students, both male and female were selected from two medical colleges on convenient basis. Sample size was determined by the Cochran equation with 95% confidence interval. A closed ended, original questionnaire was developed in English language from valid questionnaires of similar studies conducted in past. Data was analysed using MS Excel and SPSS version 22. Results: Total of 150 Data of 270 (90%) respondents was analysed after drop outs. Among the study participants 114 (42.2%) students had No knowledge about mentoring for medical education; 153 (96.8 %) students responded that mentoring would help in the academic to professional development of the students. Majority of the students (n=152, 96.2 %) respondents stated that there was a strong need of formal mentorship programme in their medical college. Conclusion: Knowledge regarding function and structure of mentorship program among students was below average. However a strong positive attitude to initiate the mentorship Programme was observed among respondents.

This study was undertaken to assess the relationship between Emotional Intelligence, Coping Styles and Psychopathology among medical students in district Peshawar, Pakistan.200 participants were recruited for data collection through purposive convenient sampling. Cross sectional research design was used. The age range of participants was 18 to 25 years with the participation of male (n=117) and female (n=83). Subjects were selected from public and private medical colleges. To determine the role of Demographic variables, age, gender, father income, college and self-reports measures of Emotional Intelligence scale Brief Cope Inventory and Psychopathic deviation were used. Correlation, t-test and regression analysis was applied for data analysis. The results revealed that female had high emotional intelligence as compared to male.it also showed from the results that those who have low emotional intelligence had more psychopathological characteristics. The results also revealed that those students who had poor emotional intelligence used maladaptive coping styles. Hence, it was inferred from the study that there is significant positive correlation between emotional intelligence, coping styles and psychopathology. Cross sectional nature of the study, use of self-report measures and non-probability sampling was the limitation of the study.

Background: Stress coping methods are used by the individuals to overcome daily stresses. It is important to maintain stress within limits for normal functioning and productivity of a human being. Medical students come across many stress factors during their medical training. Stress coping is divided into Adaptive and Non-adaptive coping, students using adaptive coping strategies (ACS) are considered to have positive coping methods and results in long term constructive outcomes i.e. improved self-esteem, stronger social bonds and wisdom, while students using non-adaptive coping strategies are at risk of mental health issues. Our objective was to evaluate stress coping methods among medical students of public and private medical colleges of district Peshawar. Methods: This study was a descriptive cross-sectional study. Data was collected using convenient sampling technique from 200 medical students of both Public and private sector of district Peshawar. Equal numbers of boys and girls were selected from age groups of 18-25 years. Demographic variable along with methods to cope stress were collected using validated pre-tested questionnaire called "Brief cope scale (BCS) of Urdu version by Akhtar (2005)". Standard cut off value of 56 was used to differentiate between students having adaptive or mal-adaptive coping styles. Data collected was analyzed using SPSS. Results: Students of public medical college scored M=71.78 with SD=8.955 while students of private medical colleges scored M=69.20 with SD 10.07. Conclusion: The findings of the study suggest that undergraduate students in medical collages have non-adaptive coping methods rather than adaptive ones. Female students used more adaptive coping skills than male students. Students in public sector had better coping skills than students of private medical colleges.