ArticleJournal of the American College of Emergency Physicians open2020
Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department.
Article in Journal of the American College of Emergency Physicians open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Which explainable AI methods in medical imaging are clinically impactful? A systematic literature review addressing the clinician's perspective.Frontiers in artificial intelligence · 2026Pooled it
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions.Journal of general internal medicine · 2026Article
- Learning health system strategies in the AI era.npj health systems · 2025Review
- Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review.EClinicalMedicine · 2025Article
- A multimodal multidomain multilingual medical foundation model for zero shot clinical diagnosis.NPJ digital medicine · 2025Article
- Effects of artificial intelligence implementation on efficiency in medical imaging-a systematic literature review and meta-analysis.NPJ digital medicine · 2024Review
- Quality of interaction between clinicians and artificial intelligence systems. A systematic review.Future healthcare journal · 2024Review
- Impact of a deep learning sepsis prediction model on quality of care and survival.NPJ digital medicine · 2024Article
- A medical multimodal large language model for future pandemics.NPJ digital medicine · 2023Article
- Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.Critical care clinics · 2023Review
- Developing a highly-reliable learning health system.Learning health systems · 2023Article
- Role of Imaging in the Management of Patients with SARS-CoV-2 Lung Involvement Admitted to the Emergency Department: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Bending the patient safety curve: how much can AI help?NPJ digital medicine · 2023Article
- Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study.Radiology. Artificial intelligence · 2022Article
- Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs.Diagnostics (Basel, Switzerland) · 2022Article
- The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThe coronavirus disease 2019 pandemic has inspired new innovations in diagnosing, treating, and dispositioning patients during high census conditions with constrained resources. Our objective is to describe first experiences of physician interaction with a novel artificial intelligence (AI) algorithm designed to enhance physician abilities to identify ground-glass opacities and consolidation on chest radiographs.
methodsDuring the first wave of the pandemic, we deployed a previously developed and validated deep-learning AI algorithm for assisted interpretation of chest radiographs for use by physicians at an academic health system in Southern California. The algorithm overlays radiographs with "heat" maps that indicate pneumonia probability alongside standard chest radiographs at the point of care. Physicians were surveyed in real time regarding ease of use and impact on clinical decisionmaking.
resultsOf the 5125 total visits and 1960 chest radiographs obtained in the emergency department (ED) during the study period, 1855 were analyzed by the algorithm. Among these, emergency physicians were surveyed for their experiences on 202 radiographs. Overall, 86% either strongly agreed or somewhat agreed that the intervention was easy to use in their workflow. Of the respondents, 20% reported that the algorithm impacted clinical decisionmaking.
conclusionsTo our knowledge, this is the first published literature evaluating the impact of medical imaging AI on clinical decisionmaking in the emergency department setting. Urgent deployment of a previously validated AI algorithm clinically was easy to use and was found to have an impact on clinical decision making during the predicted surge period of a global pandemic.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.