Artificial intelligence cooperation to support the global response to COVID-19

Swarm Learning as a privacy-preserving machine learning approach for disease classification

“…There is no doubt that numerous medical and other data types as well as a vast variety of computational approaches can be used during a pandemic 14 . We do not want to imply that blood transcriptomics would be the preferred solution for the many questions that AI and machine learning could help to solve during such a crisis.…”

“…Indeed, statements by lawmakers have been triggered, clearly indicating that privacy rules also fully apply during the pandemics (EU Digital Solidarity: a call for a pan-European approach against the pandemic, Wojciech Wiewiórowski, https://edps.europa.eu/sites/edp/files/publication /2020-04-06_eu_digital_solidarity_covid19_en.pdf, 2020). Particular in a crisis situation such as the current pandemic, AI systems need to comply with ethical principles and respect human rights 14 . We therefore argue that systems such as Swarm Learning that allow fair, transparent and still highly regulated shared data analytics while preserving data privacy regulations are to be favored, particularly during times of high urgency to develop supportive tools for medical decision making.…”

“…We therefore argue that systems such as Swarm Learning that allow fair, transparent and still highly regulated shared data analytics while preserving data privacy regulations are to be favored, particularly during times of high urgency to develop supportive tools for medical decision making. We therefore also propose to explore SL for image-based diagnostics of COVID-19 from patterns in X-ray images or computed tomography (CT) scans 21,22 , structured health records 67 , or wearables for disease tracking 14 and GSE122515 (dataset 3). Briefly, this dataset was generated by inspection of all publicly available datasets at GEO on September 20th, 2017.…”

“…Yet, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation 8 (hhs.gov, https://www.hhs.gov/hipaa/index.html, 2020; Intersoft Consulting, General Data Protection Regulation, https://gdpr-info.eu; Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data, https://rm.coe.int/16808ade9d). Particularly, in a global health crisis, as in the case of the infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leading to the pandemic spread of coronavirus disease 2019 (COVID-19) [9][10][11] , reliable, fast, secure and privacy-preserving technical solutions based on AI principles are now believed to add to the armamentarium to quickly answer important questions in the fight against such threats [12][13][14][15] . These AI-based concepts range from protein structure prediction , drug target prediction 17 , knowledge sharing 18 , tools for population control 19,20 to the assistance of healthcare personnel, e.g.…”

Moving Health Literacy Research and Practice towards a Vision of Equity, Precision and Transparency

“…In the context of the coronavirus disease of 2019 (COVID-19), an individual’s health literacy supports his/her decisions on washing hands, maintaining physical distance, adopting protective behaviours, seeing a doctor, and complying with quarantine policies, thus contributing to a more likely successful public health response strategy [ 8 , 9 , 10 ]. Health literacy also helps to navigate the infodemic—the overabundance of valid and invalid information that is circulating on the Internet—that is attached to the COVID-19 pandemic [ 11 , 12 ].…”