We introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) based on machine learning applications in medical imaging, for analyzing pandemic-induced effects on healthcare systems. By tracking both large-scale utilization and AI results data, the tru-AI approach can establish surrogates for measuring the amount of care provided and estimate the prevalence of certain disease conditions under unusual circumstances, such as pandemic outbreaks. To quantitatively evaluate our approach, we analyzed service requests for automatically identifying intracranial hemorrhage (ICH) on head CT using a commercial AI solution (Aidoc, Tel Aviv, Israel). This software is typically used for AI-based prioritization of radiologists’ reading lists for reducing turnaround times in patients with emergent clinical findings, such as ICH or pulmonary embolism. Imaging data is anonymized, uploaded to a cloud-based inference machine in real time, and AI-based ICH detection results are returned. We recorded N = 3,084 emergency-setting non-contrast head CT studies at a major US healthcare system during two observation periods, namely (i) a pre-pandemic epoch (January 1–31, 2020) and (ii) after the Covid-19 outbreak (March 15 – April 30, 2020). Although daily counts of unique imaged patients were significantly lower during (37.9 ± 7.6) than before (42.0 ± 6.2) the Covid-19 outbreak, we found that ICH was more likely to be observed during than before the Covid-19 outbreak (p<0.05). Our results suggest that, by tracking both large-scale utilization and AI results data, the tru-AI approach can contribute clinical value as an exploratory tool, aiming at a better understanding of pandemic-related effects on healthcare.
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