The urgency for telemedicine is felt during the COVID-19 pandemic which has rendered the world shut by enforcing quarantines and lockdowns. Many developing countries including Pakistan have inadequate telehealth care services that limited access to rural and remote areas. A cross-sectional survey was carried out among medical students i.e., both preclinical and clinical enrolled in various medical colleges from all provinces of Pakistan to determine their Knowledge, Attitude and Perception regarding the use of Telemedicine during the COVID-19 Pandemic. A total of 398 respondents were included in this preliminary survey. Knowledgeable scores were calculated, from a maximum obtainable score of 7. The mean knowledge was found to be significantly associated with age, province, and year of study (p-value < 0.05). Attitude scores were calculated from a maximum obtainable score of 10. All the independent variables failed to reach a significant (p < 0.05) association with the mean attitude of respondents about telemedicine. Perception scores were calculated from a maximum obtainable score of 8. Residents of Khyber Pakhtunkhwa are more likely to know about telemedicine than Balochistan (p = 0.022) on univariate regression. We identified, lack of knowledge and training for telemedicine in medical institutes. It is crucial to assess the knowledge of medical students regarding telemedicine to comprehend, and evaluate their attitude as future doctors who can play a significant role in establishing telemedicine services in the health care system.

Artificial Intelligence (AI) performs human intelligence-dependant tasks using tools such as Machine Learning, and its subtype Deep Learning. AI has incorporated itself in the field of cardiovascular medicine, and increasingly employed to revolutionize diagnosis, treatment, risk prediction, clinical care, and drug discovery. Heart failure has a high prevalence, and mortality rate following hospitalization being 10.4% at 30-days, 22% at 1-year, and 42.3% at 5-years. Early detection of heart failure is of vital importance in shaping the medical, and surgical interventions specific to HF patients. This has been accomplished with the advent of Neural Network (NN) model, the accuracy of which has proven to be 85%. AI can be of tremendous help in analyzing raw image data from cardiac imaging techniques (such as echocardiography, computed tomography, cardiac MRI amongst others) and electrocardiogram recordings through incorporation of an algorithm. The use of decision trees by Rough Sets (RS), and logistic regression (LR) methods utilized to construct decision-making model to diagnose congestive heart failure, and role of AI in early detection of future mortality and destabilization episodes has played a vital role in optimizing cardiovascular disease outcomes. The review highlights the major achievements of AI in recent years that has radically changed nearly all areas of HF prevention, diagnosis, and management.

Introduction:This review highlights the potential mechanisms of neuromuscular manifestation of COVID-19, especially myasthenia gravis (MG).Methods: An extensive literature search was conducted by two independent investigators using PubMed/MEDLINE and Google Scholar from its inception to December 2020. Results: Exacerbations of clinical symptoms in patients of MG who were treated with some commonly used COVID-19 drugs has been reported, with updated recommendations of management of symptoms of neuromuscular disorders. Severe acute respiratory syndrome coronavirus 2 can induce the immune response to trigger autoimmune neurological disorders. Conclusions: Further clinical studies are warranted to indicate and rather confirm if MG in the setting of COVID-19 can pre-existent subclinically or develop as a new-onset disease.

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