ReviewAnnals of translational medicine2025
Artificial intelligence-driven diagnosis of acute thoracic aortic dissection: integrating imaging, biomarkers, and clinical workflows-a narrative review.
Review in Annals of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Time Is Aorta: Can Artificial Intelligence Improve Surgical Timelines in Acute Type A Aortic Dissection? A Comprehensive Review.Health science reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Patients presenting to the emergency department with acute thoracic aortic dissection (ATAD) often experience chest pain that requires urgent intervention. However, other chest pain-related emergencies, such as acute coronary syndrome (ACS) and acute pulmonary embolism (PE), are far more common and frequently overshadow ATAD. This disparity leads to a high rate of ATAD misdiagnosis. Recent advancements in artificial intelligence (AI) have led to the development of various models utilizing imaging modalities and biomarkers to enable rapid triage and diagnosis of ATAD in emergency settings. This article aims to evaluate the performance and clinical significance of these AI models within the context of clinical workflows. Methods: We performed literature searches in PubMed, Scopus, and Web of Science to identify relevant studies published between 2015 and 2025, with the focus of the differentiation of ATAD patients from other chest pain-related conditions in emergency settings, with the application of AI. Key Content and Findings: Eighteen studies were retrieved from the past ten years, highlighting a significant knowledge gap in the field of translational medicine. The discussion included an overview of AI-powered models for ATAD diagnosis, as well as guidelines on current clinical workflows and the application of AI in clinical settings. Conclusions: This article offers a detailed review of AI models developed for the screening and diagnosis of ATAD. It highlights not only the performance of these technologies but also their clinical importance in facilitating timely interventions for high-risk patients. Looking forward, we anticipate a future where AI and deep learning (DL)-driven ATAD diagnostic models will play a pivotal role in optimizing ATAD clinical management.
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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.