In a recent study published in PLOS Digital Health, researchers reviewed existing literature on the use of artificial intelligence (AI) in health care to characterize the AI applications used in the clinical applications during the coronavirus disease 2019 (COVID-19) pandemic, investigate the location, timing, and extent of AI use in healthcare, and examine the United States (U.S.) regulatory approval processes. Despite the large number of approvals granted by the U.S. Food and Drug Administration (FDA) to AI applications in healthcare in the last six years, the adoption of AI applications in different areas of healthcare has been limited. Furthermore, there is limited information on the development and use of AI applications during the COVID-19 pandemic, unlike the significant and rapid growth in telehealth and vaccine technologies. While previous reviews have reviewed the potential uses, challenges, and impacts of AI applications for COVID-19 clinical response, many of the reviews found methodological flaws and potential biases in the use of AI applications in clinical practice. A scarcity of reviews provides a comprehensive report on the development, testing, and applications of AI in COVID-19 clinical responses.

Author summary In this study we describe the use of artificial intelligence (AI) in the clinical response to COVID-19. AI has been variously predicted to play a key role during the pandemic or has been reported to have had little or no impact on patient care. Our findings support a balanced view. We identified 66 applications—specific AI products or tools—used in a variety of ways to diagnose, guide treatment, or prioritize patients during the pandemic response. Many were deployed early in 2020 and most were used in the U.S., other high-income countries, or China. Some were used to care for hundreds of thousands of patients though most were adopted at smaller scales. We found evaluation studies that supported the use of 39 of these applications, though few of these evaluations were written by independent authors, not affiliated with application developers. We found no clinical trials that evaluated the effect of using an AI application on patient health outcomes. Future research is needed to better understand the impact of using AI in clinical care.

Hydralazine order and first-dosing times were nonuniformly distributed over 24 h [Kuiper's test (3), P 0.01], marked by distinct morning-time surges and overnight lulls (Figure 1). Nearly twice as many treatments were ordered between 8:00 AM and 6:00 PM (2,842) compared to 10:00 PM and 8:00 AM (1,652). The profiles were described by 24-h rhythms using 3 separate detection methods [cosinor analysis (4), Jonckheere-Terpstra-Kendall (JTK)_CYCLE (5, 6), and Rhythmicity Analysis Incorporating Nonparametric (RAIN) (7), P 0.05] (Figure 1B and Dataset S1). The morning surge in hydralazine order times coincides with team rounding and a medical staff shift change (Figure 1C). Caretaker shifts at our institution include 7:00 AM to 7:00 PM, 7:00 AM to 3:00 PM, and 7:00 PM to 7:00 AM shifts.

Clinical trial data can provide a wealth of information about how drugs work. Yet such information often belongs to pharmaceutical companies and is rarely accessible to the scientific community at large. Cristescu et al. provide exploratory analysis of a cancer genomics dataset, collected from four separate clinical trials of Merck's PD-1 immunotherapy drug, pembrolizumab. This informative public resource examines more than 300 patient samples representing 22 different tumor types. Two widely used signatures that currently predict immunotherapy response are tumor mutational burden and a "hot" T cell–inflamed microenvironment. The study analyzed these two proposed biomarkers in combination to see what predictive clinical utility they may hold. Immunotherapy targeting the programmed cell death protein–1 (PD-1) axis elicits durable antitumor responses in multiple cancer types. However, clinical responses vary, and biomarkers predictive of response may help to identify patients who will derive the greatest therapeutic benefit. Clinically validated biomarkers predictive of response to the anti–PD-1 monoclonal antibody pembrolizumab include PD-1 ligand 1 (PD-L1) expression in specific cancers and high microsatellite instability (MSI-H) regardless of tumor type. Tumor mutational burden (TMB) and T cell–inflamed gene expression profile (GEP) are emerging predictive biomarkers for pembrolizumab. Both PD-L1 and GEP are inflammatory biomarkers indicative of a T cell–inflamed tumor microenvironment (TME), whereas TMB and MSI-H are indirect measures of tumor antigenicity generated by somatic tumor mutations. However, the relationship between these two categories of biomarkers is not well characterized.