Abstract
Post-COVID conditions (PCC) emerged during the pandemic, prompting a rise in the use of Digital Health Technologies (DHTs) to manage lockdowns and hospital overcrowding. Real-time tracking and information analyses were crucial to strengthening the global research response. This study aims to map the use of modern digital approaches in estimating the prevalence, predicting, diagnosing, treating, monitoring, and prognosis of PCC. This review was conducted by searching PubMed and Scopus databases for keywords and synonyms related to DHTs, Smart Healthcare Systems, and PCC based on the World Health Organization definition. Articles published from 1 January 2020 to 21 May 2024 were screened for eligibility based on predefined inclusion criteria, and the PRISMA framework was used to report the findings from the retained studies. Our search identified 377 studies, but we retained 23 studies that used DHTs, artificial intelligence (AI), and infodemiology to diagnose, estimate prevalence, predict, treat, and monitor PCC. Notably, a few interventions used infodemics to identify the clinical presentations of the disease, while most utilized Electronic Health Records and AI tools to estimate diagnosis and prevalence. However, we found that AI tools were scarcely used for monitoring symptoms, and studies involving SHS were non-existent in low- and middle-income countries (LMICs). These findings show several DHTs used in healthcare, but there is an urgent need for further research in SHS for complex health conditions, particularly in LMICs. Enhancing DHTs and integrating AI and infodemiology provide promising avenues for managing epidemics and related complications, such as PCC.