Abstract

BackgroundRheumatological and dermatological disorders contribute to a significant portion of the global burden of disease. Big Data are increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including dermatology and rheumatology. Rheumatology and dermatology can potentially benefit from Big Data.MethodsA systematic review of the literature was conducted according to the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines, mining “Uno per tutti”, a highly integrated and automated tool/meta-database developed at the University of Genoa, Genoa, Italy, and consisting of 20 major scholarly electronic databases, including PubMed/MEDLINE. Big Data- or artificial intelligence-based studies were judged based on the modified Qiao’s critical appraisal tool for critical methodological quality assessment of Big Data/machine learning-based studies. Other studies designed as cross-sectional, longitudinal, or randomized investigations, reviews/overviews or expert opinions/commentaries were evaluated by means of the relevant “Joanna Briggs Institute” (JBI)’s critical appraisal tool for the critical methodological quality assessment.ResultsFourteen papers were included in the present systematic review of the literature. Most of the studies included concerned molecular applications of Big Data, especially in the fields of genomics and post-genomics. Other studies concerned epidemiological applications, with a practical dearth of studies assessing smart and digital applications for psoriatic arthritis patients.ConclusionsBig Data can be a real paradigm shift that revolutionizes rheumatological and dermatological practice and clinical research, helping to early intercept psoriatic arthritis patients. However, there are some methodological issues that should be properly addressed (like recording and association biases) and some ethical issues that should be considered (such as privacy). Therefore, further research in the field is warranted.Systematic Review RegistrationRegistration code 10.17605/OSF.IO/4KCU2.

Highlights

  • The global burden of disease (GBD) is the quantitative assessment of the health loss due to a given disorder, risk factor, or injury, over a span of time, worldwide

  • To the best of our knowledge, this review represents the first systematic review addressing the current state of the use of Big Data, Artificial intelligence, digital and smart technologies for psoriatic arthritis

  • Epidemiological/clinical Big Data can come from large-scale, often nationwide surveys

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Summary

Introduction

The global burden of disease (GBD) is the quantitative assessment of the health loss due to a given disorder, risk factor, or injury, over a span of time, worldwide. Geo-spatially and temporally modeled and computed as the epidemiological, clinical, and societal burden imposed by a given disease, taking into account its economicfinancial and humanistic effects, if inadequately managed and treated Such a quantitative and broad approach enables practitioners and researchers as well as all relevant stakeholders, including public and global health decision- and policymakers, to consistently compare the burden of different diseases, risk factors, or injuries, over time and across countries. These data can guide health policies, guiding them in a pure data-driven and evidence-based way, enabling them to prioritize and allocate resources, especially in developing countries and in other resource-limited settings [1].

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