Artificial intelligence applications used in the clinical response to COVID-19: A scoping review

Abstract

Research into using artificial intelligence (AI) in health care is growing and several observers predicted that AI would play a key role in the clinical response to the COVID-19. Many AI models have been proposed though previous reviews have identified only a few applications used in clinical practice. In this study, we aim to (1) identify and characterize AI applications used in the clinical response to COVID-19; (2) examine the timing, location, and extent of their use; (3) examine how they relate to pre-pandemic applications and the U.S. regulatory approval process; and (4) characterize the evidence that is available to support their use. We searched academic and grey literature sources to identify 66 AI applications that performed a wide range of diagnostic, prognostic, and triage functions in the clinical response to COVID-19. Many were deployed early in the pandemic and most were used in the U.S., other high-income countries, or China. While some applications were used to care for hundreds of thousands of patients, others were used to an unknown or limited extent. We found studies supporting the use of 39 applications, though few of these were independent evaluations and we found no clinical trials evaluating any application's impact on patient health. Due to limited evidence, it is impossible to determine the extent to which the clinical use of AI in the pandemic response has benefited patients overall. Further research is needed, particularly independent evaluations on AI application performance and health impacts in real-world care settings.

Fig 1. PRISMA Flow Diagram.

Fig 2. Date and country of first confirmed deployment in the COVID-19 response, by application category.

Note: Dates have been jittered up to 12 days within the same month.

Fig 3. Extent and location of use in the COVID-19 response, by application category.

Note: Scale of use reflects numbers of patients or related proxy measures. 10 applications were used in multiple country categories and are represented by multiple bubbles. We were unable to find exact country or scale of use for 3 online symptom checkers; these are represented by ‘unknown’ bubbles assigned to the country category where they were developed.

Fig 4. Evaluation studies of applications, by study conclusion and publication type.

Note: Multiple studies on a single application are represented by a cluster of bubbles. Nine documents are each represented by two separate bubbles in this figure, including 3 documents that present evidence on two applications and 6 that presented evidence from both a validation study and a non-validation (‘other’) type of study.