Managing the Infodemic: Leveraging Deep Learning to Evaluate the Maturity Level of AI-Based COVID-19 Publications for Knowledge Surveillance and Decision Support

COVID-19 pandemic has taught us many lessons, including the need to manage the exponential growth of knowledge, fast-paced development or modification of existing AI models, limited opportunities to conduct extensive validation studies, the need to understand bias and mitigate it, and lastly, implementation challenges related to AI in healthcare. While the nature of the dynamic pandemic, resource limitations, and evolving pathogens were key to some of the failures of AI to help manage the disease, the infodemic during the pandemic could be a key opportunity that we could manage better. We share our research related to the use of deep learning methods to quantitatively and qualitatively evaluate AI-based COVID-19 publications which provides a unique approach to identify “mature” publications using a validated model and how that can be leveraged further by focused human-in-loop analysis. The study utilized research articles in English that were human-based, extracted from PubMed spanning the years 2020 to 2022. The findings highlight notable patterns in publication maturity over the years, with consistent and significant contributions from China and the United States. The analysis also emphasizes the prevalence of image datasets and variations in employed AI model types. To manage an infodemic during a pandemic, we provide a specific knowledge surveillance method to identify key scientific publications in near real-time. We hope this will enable data-driven and evidence-based decisions that clinicians, data scientists, researchers, policymakers, and public health officials need to make with time sensitivity while keeping humans in the loop. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement We acknowledge the funding support by Pathcheck foundation awarded to Dr. Aditya Nagori and Mr.Raghav Awasthi. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors