ArticleNPJ digital medicine2023
Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- How Does Medical Artificial Intelligence Revolutionize Physician Productivity?Yonsei medical journal · 2026Pooled it
- Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- A systematic review of machine learning models for management, prediction and classification of ARDS.Respiratory research · 2024Pooled it
- The Acute Respiratory Distress Syndrome (ARDS): epidemiology, etiology, molecular mechanisms, diagnosis and therapeutic strategies.Molecular biomedicine · 2026Review
- Evaluating an AI-driven Triaging Workflow for MRI-based Clinically Significant Prostate Cancer Diagnosis: A Simulation Study.Radiology. Imaging cancer · 2026Observational
- CT-based AI system for quantitative and integrated management of acute respiratory distress syndrome in critical care.NPJ digital medicine · 2026Article
- Artificial intelligence for early diagnosis in emergency department.Journal of anesthesia, analgesia and critical care · 2026Review
- Artificial intelligence-assisted prediction of Demodex mite density in facial erythema.Scientific reports · 2025Article
- Incorporating Machine Learning Techniques to Enhance Rodent Surveillance in Marginalized Urban Communities.Ecology and evolution · 2025Article
- [Acute respiratory distress syndrome-quo vadis : Innovative and individualized treatment approaches].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2025Review
- Navigating the AI Revolution in Medicine-Adopting Strategies for Medical Education.Medical science educator · 2025Article
- Advances in acute respiratory distress syndrome: focusing on heterogeneity, pathophysiology, and therapeutic strategies.Signal transduction and targeted therapy · 2025Review
- Heterogeneity and predictors of the effects of AI assistance on radiologists.Nature medicine · 2024Article
- Leveraging Multi-Annotator Label Uncertainties as Privileged Information for Acute Respiratory Distress Syndrome Detection in Chest X-ray Images.Bioengineering (Basel, Switzerland) · 2024Article
- Lung Imaging and Artificial Intelligence in ARDS.Journal of clinical medicine · 2024Review
- Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.Bioengineering (Basel, Switzerland) · 2023Article
- Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches.NPJ digital medicine · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
There is a growing gap between studies describing the capabilities of artificial intelligence (AI) diagnostic systems using deep learning versus efforts to investigate how or when to integrate AI systems into a real-world clinical practice to support physicians and improve diagnosis. To address this gap, we investigate four potential strategies for AI model deployment and physician collaboration to determine their potential impact on diagnostic accuracy. As a case study, we examine an AI model trained to identify findings of the acute respiratory distress syndrome (ARDS) on chest X-ray images. While this model outperforms physicians at identifying findings of ARDS, there are several reasons why fully automated ARDS detection may not be optimal nor feasible in practice. Among several collaboration strategies tested, we find that if the AI model first reviews the chest X-ray and defers to a physician if it is uncertain, this strategy achieves a higher diagnostic accuracy (0.869, 95% CI 0.835-0.903) compared to a strategy where a physician reviews a chest X-ray first and defers to an AI model if uncertain (0.824, 95% CI 0.781-0.862), or strategies where the physician reviews the chest X-ray alone (0.808, 95% CI 0.767-0.85) or the AI model reviews the chest X-ray alone (0.847, 95% CI 0.806-0.887). If the AI model reviews a chest X-ray first, this allows the AI system to make decisions for up to 79% of cases, letting physicians focus on the most challenging subsets of chest X-rays.
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What OpenQuestion holds
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.