Online teaching and learning is not a new phenomenon. For the last many years, it has been used but mainly as a part of face to face teaching. Assessment is an essential part of teaching and learning, as it establishes the achievement of course learning outcomes. Computer-based assessment, mainly summative is in place for a long time now, however, online formative and summative assessments have been less practiced. This is because of the issues of validity, reliability and dishonesty. During the COVID 19 pandemic, the educational environment has taken a paradigm shift in many medical schools, both nationally and internationally. This situation demands a method of assessment that is safe, valid, reliable, acceptable, feasible and fair. This paper describes the different formats of online assessment and their application in formative and summative assessments during and after the COVID 19 pandemic. doi: https://doi.org/10.12669/pjms.36.COVID19-S4.2795 How to cite this:Khan RA, Jawaid M. Technology Enhanced Assessment (TEA) in COVID 19 Pandemic. Pak J Med Sci. 2020;36(COVID19-S4):---------.  doi: https://doi.org/10.12669/pjms.36.COVID19-S4.2795 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low-or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI).Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression.

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doi: https://doi.org/10.12669/pjms.36.5.3042 How to cite this:Imran N, Jawaid M. Artificial intelligence in medical education: Are we ready for it? Pak J Med Sci. 2020;36(5):857-859.  doi: https://doi.org/10.12669/pjms.36.5.3042 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The significant psychological morbidity and distress warrants establishing support systems to support students and bringing about evidence-based changes to teaching and evaluation systems. Adequate counseling facilities should be made available and students encouraged to seek help.