Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in Hunan wildlife market, Wuhan, China . The infection is caused by a coronavirus, SARS-CoV-2 and clinically characterized by common symptoms including fever, dry cough, loss of taste/smell, myalgia and pneumonia in severe cases . With overwhelming spikes in infection and death, its pathogenesis yet remains elusive . Since the infection spread rapidly, its healthcare demands are overwhelming with uncontrollable emergencies . Although laboratory testing and analysis are developing at an enormous pace, the high momentum of severe cases demand more rapid strategies for initial screening and patient stratification . Several molecular biomarkers like C-reactive protein, interleukin-6 (IL6), eosinophils and cytokines, and artificial intelligence (AI) based screening approaches have been developed by various studies to assist this vast medical demand . This review is an attempt to collate the outcomes of such studies, thus highlighting the utility of AI in rapid screening of molecular markers along with chest X-rays and other COVID-19 symptoms to enable faster diagnosis and patient stratification . By doing so, we also found that molecular markers such as C-reactive protein, IL-6 eosinophils, etc . showed significant differences between severe and non-severe cases of COVID-19 patients . CT findings in the lungs also showed different patterns like lung consolidation significantly higher in patients with poor recovery and lung lesions and fibrosis being higher in patients with good recovery . Thus, from these evidences we perceive that an initial rapid screening using integrated AI approach could be a way forward in efficient patient stratification.