text: A narrative review on characterization of acute respiratory distress syndrome in COVID 19 infected lungs using artificial intelligence COVID 19 has infected 77 4 million people worldwide and has caused 1 7 million fatalities as of December 21 2020 The primary cause of death due to COVID 19 is Acute Respiratory Distress Syndrome ARDS According to the World Health Organization WHO people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS CoV 2 Medical imaging provides a non invasive touch free and relatively safer alternative tool for diagnosis during the current ongoing pandemic Artificial intelligence AI scientists are developing several intelligent computer aided diagnosis CAD tools in multiple imaging modalities i e lung computed tomography CT chest X rays and lung ultrasounds These AI tools assist the pulmonary and critical care clinicians through a faster detection of the presence of a virus b classifying pneumonia types and c measuring the severity of viral damage in COVID 19 infected patients Thus it is of the utmost importance to fully understand the requirements of for a fast and successful and timely lung scans analysis This narrative review first presents the pathological layout of the lungs in the COVID 19 scenario followed by understanding and then explains the comorbid statistical distributions in the ARDS framework The novelty of this review is the approach to classifying the AI models as per the by school of thought SoTs exhibiting based on segregation of techniques and their characteristics The study also discusses the identification of AI models and its extension from non ARDS lungs pre COVID 19 to ARDS lungs post COVID 19 Furthermore it also presents AI workflow considerations of for medical imaging modalities in the COVID 19 framework Finally clinical AI design considerations will be discussed We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor Furthermore ARDS post processing clinical systems must involve include i the clinical validation and verification of AI models ii reliability and stability criteria and iii easily adaptable and iv generalization assessments of AI systems for their use in pulmonary critical care and radiological settings 