Introduction: Among the most damaging characteristics of the Covid-19 pandemic has been its disproportionate effect on disadvantaged communities in terms of infection rate, hospitalization and mortality. Emerging data from the literature and preprint articles suggest that black and minorities are at an increased risk of acquiring Covid-19 infection compared to White individuals and also worse clinical outcomes from Covid-19. Artificial intelligence (AI) is an emerging technology which aims to guide clinical decision making for this emerging disease. However, it may be susceptible to algorithmic biases that can entrench and augment existing inequality.

OBJECTIVES: To review published articles and reports of prediction models for diagnosing Covid-19 in patients with suspected infection, for prognosis of patients with Covid-19, and for detecting people in the general population at increased risk of becoming infected with Covid-19 or being admitted to hospital with the disease using AI technology models.

METHODS: Pubmed publications and studies that developed or validated a multivariable Covid-19 related prediction model using AI technology modes.

RESULTS: Intrinsic to transparency, the source code of any AI model should be shared publicly to ensure models can be broadly applied, generalized, and compared. Strict reporting standards will facilitate the deployment of emerging Covid-19 AI models as clinical decision support tools by allowing healthcare systems to understand and mitigate potential biases associated with a particular AI model.

CONCLUSIONS: There is an urgent need to enforce the systematic use of reporting standards and develop regulatory frameworks for a shared Covid-19 data source to address the challenges of bias in AI during this pandemic. There is hope that AI can help guide treatment decisions within this crisis; yet given the pervasiveness of biases, a failure to proactively develop comprehensive mitigation strategies during the COVID-19 pandemic risks exacerbating existing health disparities.