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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Barrios, J. P., Ansari, M., Olgin, J. E., Abreau, S., Delfrate, J., Labrecque Langlais, É., Avram, R., & Tison, G. H. (2026). Multiview deep learning improves detection of major cardiac conditions from echocardiography. Nature Cardiovascular Research, 5(3), 234-245. External link
Thériault-Lauzier, P., Corbin, D., Tastet, O., Labrecque Langlais, É., Taji, B., Kang, G., Chong, A.-Y., So, D., Tang, A., Wawira Gichoya, J., Anbil Parthipan, S. C., Déziel, P.-L., Hussin, J. G., Kadoury, S., & Avram, R. (2025). Erratum: "A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform Canadian Journal of Cardiology Volume 40, Issue 10, October 2024, Pages 1828-1840". Canadian Journal of Cardiology. Available
Labrecque Langlais, É., Corbin, D., Tastet, O., Hayek, A., Doolub, G., Mrad, S., Tardif, J.-C., Tanguay, J.-F., Marquis-Gravel, G., Tison, G. H., Kadoury, S., Le, W., Gallo, R., Lesage, F., & Avram, R. (2024). Evaluation of stenoses using AI video models applied to coronary angiography. NPJ Digital Medicine, 7, 138 (13 pages). Available
Yurk, D., Barrios, J. P., Labrecque Langlais, E., Avram, R., Aras, M. A., Abu-Mostafa, Y., Padmanabhan, A., & Tison, G. H. (2024). Automated Assessment of Right Atrial Pressure From Ultrasound Videos Using Machine Learning. JACC: Advances, 3(9, Part 2), 101192 (10 pages). External link
Nolin-Lapalme, A., Thériault-Lauzier, P., Corbin, D., Tastet, O., Sharma, A., Hussin, J. G., Kadoury, S., Jiang, R., Krahn, A. D., Gallo, R., & Avram, R. (2024). Maximizing Large Language Model Utility in Cardiovascular Care: A Practical Guide. Canadian Journal of Cardiology, 38 pages. External link
Thériault-Lauzier, P., Corbin, D., Tastet, O., Langlais, É. L., Taji, B., Kang, G., Chong, A.-Y., So, D., Tang, A., Gichoya, J. W., Anbil Parthipan, S. C., Déziel, P.-L., Hussin, J. G., Kadoury, S., & Avram, R. (2024). A responsible framework for applying artificial intelligence on medical images and signals at the point-of-care: the PACS-AI platform. Canadian Journal of Cardiology, 025 (39 pages). Available
Doolub, G., Khurshid, S., Thériault-Lauzier, P., Nolin Lapalme, A., Tastet, O., So, D., Labrecque Langlais, E., Cobin, D., & Avram, R. (2024). Revolutionizing Acute Cardiac Care with Artificial Intelligence: Opportunities and Challenges. Canadian Journal of Cardiology, 011 (41 pages). External link
Langlais, É. L., Thériault-Lauzier, P., Marquis-Gravel, G., Kulbay, M., So, D. Y., Tanguay, J.-F., Ly, H. Q., Gallo, R., Lesage, F., & Avram, R. (2023). Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications. Journal of Cardiovascular Translational Research, 16(3), 513-525. External link
