Trial reportNature medicine2026
Generative AI-based low-dose digital subtraction angiography for intra-operative radiation dose reduction: a randomized controlled trial.
Trial report in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Article
- Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework.Healthcare (Basel, Switzerland) · 2026Review
- Bibliometric and visual analysis: A comprehensive tracking of global research trends on ablation and tumor immune microenvironment from 2016 to 2025.Translational oncology · 2026Article
- Ultra-Low-Dose Noise-Free Technology to Reduce Radiation Exposure During Coronary Angiography.Circulation. Cardiovascular interventions · 2026Observational
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
31 authors.
Funding
Abstract
Digital subtraction angiography (DSA) devices guide procedures across numerous diseases, performed on more than 100,000 patients daily worldwide. However, these procedures expose patients and healthcare providers to radiation, increasing the risk of health issues. Despite many low-dose DSA imaging methods proposed, none have been prospectively clinically validated. In this study, 46,829 patients (over 5 million DSA images) from 70 centers were used to iterate our previously developed generative artificial intelligence system (named GenDSA-V2). A total of 1,068 patients (533 in intervention arm and 535 in control arm), with suspected cerebral aneurysms (n = 435), lung cancer (n = 417) or advanced liver cancer (n = 216), meeting surgical criteria, were enrolled to validate the GenDSA-V2. The primary outcome was radiation dose, while secondary outcomes included efficiency, operation time and intraoperative complications. Group assignments were blinded to patients, surgeons and investigators, while technicians were aware but not involved in data collection or analysis. The GenDSA-V2 group showed substantially reduced radiation exposure, with an air kerma (AK) of 151.3 ± 125.1 mGy compared to 457.4 ± 407.4 mGy in the standard clinical protocols (SCP) group (mean difference = -306.1 mGy, 95% confidence interval (CI) = -342.3 to -269.9, P < 0.001 for superiority) and a dose-area product (DAP) of 4009.7 ± 2767.9 μGy m
Indexed as
Identifiers
41482562What 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.