ASSESSMENT OF THE COMMUNICATIVE ABILITIES OF STUDENTS IN MEDICAL SPECIALTIES DURING THE ONLINE TRAINING PERIOD

Purpose: Communication skills are very important in the medical profession. Challenging for the teachers to develop the communication skills of future medical specialists through online classes during Covid-19. Aim: To study the opinion of students about the importance of communication skills and its development through online classes during Covid-19, as well as to know the factors hindering the development of the communication process. Materials and methods: A survey was conducted among 81 students in the field of "Health Care" majoring in "Nursing", "Midwifery" and "Medical Assisting" through a questionnaire. The data has been analyzed with the help of SPSS. Conclusion: 67% of the students indicated that online learning will hamper the development of their much-needed communication skills to communicate with the patients. The results show difficulties in the implementation of quality health care which must be overcome. There is a need to improve the communication skills of the health care students by conducting special courses to overcome the deficiencies of online learning during Covid-19. Communication skills are extremely important in the medical profession. It is challenging for teachers to develop the communication skills of future medical professionals through online classes during Covid-19. In the present study, students' opinion about the importance of communication skills and their acquisition during the pandemic through online classes, as well as the factors hindering the development of communication skills were studied. The present study reveals that most students majoring in nursing, midwifery and medical assisting lack communication skills, particularly in their communication with patients with disabilities. This problem is related to the flaws in the educational system for medical professionals, which prepares them to work in the real world. It is established that a need for approbation and introduction of educational programs to develop the communication skills and to overcome the deficiencies of the online training during Covid-19 in this regard.

ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans

The development of medical assisting tools based on artificial intelligence advances is essential in the global fight against COVID-19 outbreak and the future of medical systems. In this study, we introduce ai-corona, a radiologist-assistant deep learning framework for COVID-19 infection diagnosis using chest CT scans. Our framework incorporates an EfficientNetB3-based feature extractor. We employed three datasets; the CC-CCII set, the MasihDaneshvari Hospital (MDH) cohort, and the MosMedData cohort. Overall, these datasets constitute 7184 scans from 5693 subjects and include the COVID-19, non-COVID abnormal (NCA), common pneumonia (CP), non-pneumonia, and Normal classes. We evaluate ai-corona on test sets from the CC-CCII set, MDH cohort, and the entirety of the MosMedData cohort, for which it gained AUC scores of 0.997, 0.989, and 0.954, respectively. Our results indicates ai-corona outperforms all the alternative models. Lastly, our framework’s diagnosis capabilities were evaluated as assistant to several experts. Accordingly, We observed an increase in both speed and accuracy of expert diagnosis when incorporating ai-corona’s assistance.