This is a conceptual paper

OBJECTIVES

: This perspective emphasizes significant developments and clinical development concepts throughout the Covid-19 era.

METHODS

: The current research is based on the observation and analysis of previously published data on the impact of COVID-19 on the management of clinical trials.

RESULTS: and

CONCLUSIONS

: The present COVID-19 outbreak is disrupting clinical research everywhere not only by deviating resourced towards SARS-CoV-2 vaccine development, but also complicating how clinical trials were conducted and introducing confounding medical conditions to consider as part of trial management. Due to social isolation and quarantines, participants and trial staff have been unable to meet in person for scheduled visits. COVID-19's quick onset and significant influence on clinical trials and research necessitate prompt attention. As a result, the FDA issued a new clinical study guideline focusing on the safety of trial participants on March 18, 2020. The FDA encouraged sponsors to explore whether additional methods of safety evaluation, such as telephone communication, virtual visits, and alternative locations for assessments, are warranted. Sponsors may use central and remote monitoring programs to maintain oversight of clinical sites. The landscape of clinical trial management has been permanently changed. Health-care professionals and governments throughout the world have been compelled to speed up artificial intelligence (AI) tool development in response to the pandemic. AI-driven technology has the potential to revolutionize every stage of the clinical trial process, from recruitment through drug adherence. This viewpoint highlights new advances and clinical development ideas. This includes adverse event detection, trial recruitment, and the repurposing of drugs. AI addresses the issues COVID introduced in clinical trials management by bringing efficiencies, providing virtual options for clinical trials management to expand recruitment and follow up activities, and utilizing Machine learning (ML)/AI in subject selection/management.