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Unraveling the Invisible: Topological Data Analysis as the New Frontier in Radiology's Diagnostic Arsenal.
Tomography
January 2025
Department of Radiology, University of Padova, 35127 Padova, Italy.
This commentary examines Topological Data Analysis (TDA) in radiology imaging, highlighting its revolutionary potential in medical image interpretation. TDA, which is grounded in mathematical topology, provides novel insights into complex, high-dimensional radiological data through persistent homology and topological features. We explore TDA's applications across medical imaging domains, including tumor characterization, cardiovascular imaging, and COVID-19 detection, where it demonstrates 15-20% improvements over traditional methods.
View Article and Find Full Text PDFArtificial intelligence improves bronchoscopy performance: a randomised crossover trial.
ERJ Open Res
January 2025
Copenhagen Academy for Medical Education and Simulation, Rigshospitalet, The Capital Region of Denmark, Copenhagen, Denmark.
Rationale: Flexible bronchoscopy is an operator-dependent procedure. An automatic bronchial identification system based on artificial intelligence (AI) could help bronchoscopists to perform more complete and structured procedures through automatic guidance.
Methods: 101 participants were included from six different continents at the European Respiratory Society annual conference in Milan, 9-13 September 2023.
Transforming Healthcare: Artificial Intelligence (AI) Applications in Medical Imaging and Drug Response Prediction.
Genome Integr
January 2025
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Artificial intelligence (AI) offers a broad range of enhancements in medicine. Machine learning and deep learning techniques have shown significant potential in improving diagnosis and treatment outcomes, from assisting clinicians in diagnosing medical images to ascertaining effective drugs for a specific disease. Despite the prospective benefits, adopting AI in clinical settings requires careful consideration, particularly concerning data generalisation and model explainability.
View Article and Find Full Text PDFLarge Language Models in Diabetes Management: The Need for Human and Artificial Intelligence Collaboration.
Diabetes Care
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Center for the Study of Aging and Human Development, Duke University, Durham, NC.
Editorial Comment: Using Artificial Intelligence to Improve the Accuracy and Efficiency of Interpreting Thousands of Images in a CT Study.
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Chairman of Radiology Med Star Health.
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