Artificial Intelligence for COVID-19: Rapid Review

Abstract

Background COVID-19 was first discovered in December 2019 and has since evolved into a pandemic. Objective To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19. Methods We performed an extensive search of the PubMed and EMBASE databases for COVID-19–related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted. Results In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19. Conclusions In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids

“… 23 , 24 , 25 , 26 Machine learning (ML) represents one means by which the likely effectiveness of specific treatments in a given individual may be predicted. 27 While ML has been applied to diverse tasks related to COVID-19, 28 within the context of therapeutics this technology has overwhelming been used to identify novel and repurposed drugs which may be effective in treating COVID-19, 28 , 29 , 30 , 31 , 32 , 33 , 34 rather than to identify which patients are most likely to experience a survival benefit from available treatments. To fill this gap, we present a pair of machine learning algorithms (MLAs) to encourage precision in the use of remdesivir or dexamethasone and related corticosteroids to treat COVID-19 patients using readily available data derived from electronic health records (EHRs).…”

The perspectives of biomarker-based electrochemical immunosensors, artificial intelligence and the Internet of Medical Things toward COVID-19 diagnosis and management

“…Blockchain technology in conjunction with AI has been developed for COVID-19 surveillance that is utilized for storing the health care–associated data [ 220 ]. One more significant use of AI is the remote monitoring of home-quarantined families and patients through smart bracelets or smartphones that provide an automatic alarm or warning message’ sound on breaking of quarantine [ 221 ]. Therefore, fast progression of automated diagnostic techniques based on AI not only serve enhanced diagnostic speed and accuracy, but rather decrease the burden and protect the healthcare staffs by reducing their contacts with patients suffering from COVID-19 and work more effectively in a harmless environment.…”

WHO guidance on ethics in outbreaks and the COVID-19 pandemic: a critical appraisal

“…41 Nevertheless, technology can also be a useful means of managing data, and AI could help in developing risk analysis models and processes and even help critically appraise all the preprints and publications currently submitted to various journals. AI models have already been developed (and are being used) as screening tools in hospital settings or to predict disease severity, 42 even as experts have cautioned on the need for validation of the tools and for greater transparency. 43 It is necessary to analyse the impact of digital technology on the pandemic response at a national and global level and to identify the ethical values that ought to drive this digital revolution.…”