Publications by authors named "Diego Ardila"

Article Synopsis
  • - Over 85 million CT scans are done annually in the US, with a significant portion focused on the abdomen, highlighting a need for efficient interpretation methods due to a shortage of radiologists.
  • - To address this, researchers introduced Merlin, a 3D Vision Language Model (VLM) that uses both electronic health records and radiology reports for training without the need for manual annotations, utilizing a vast clinical dataset of millions of images and codes.
  • - Merlin was evaluated on various tasks, including chronic disease prediction and report generation, showing better performance than current methods, demonstrating its potential to support radiologists in their work.
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Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. We investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction.

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Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and Methods An AI assistant for lung cancer screening was evaluated on two retrospective randomized multireader multicase studies where 627 (141 cancer-positive cases) low-dose chest CT cases were each read twice (with and without AI assistance) by experienced thoracic radiologists (six U.S.

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With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States. Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20-43% and is now included in US screening guidelines. Existing challenges include inter-grader variability and high false-positive and false-negative rates.

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The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a.

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