Arriel Benis on Muck Rack

Arriel Benis

As seen in: Nature, MDPI
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Articles

Advancing Global Digital Healthcare Innovation through One Digital Health and Multilingual Ontologies

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By Arriel Benis
| ami.info.umfcluj.ro
Abstract The digital transformation of healthcare offers significant opportunities alongside complex challenges. One Digital Health (ODH) provides a unified framework integrating artificial intelligence, data science, and healthcare informatics to enhance decision-making, interoperability, and sustainability within health ecosystems.
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Digital Health Education for the Future: The SaNuRN (Santé Numérique Rouen-Nice) Consortium’s Journey

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By Julien Grosjean, Frank Dufour, Arriel Benis, Holon Institut
| hal.science
Article Dans Une Revue JMIR Medical Education Année : 2024 Digital Health Education for the Future: The SaNuRN (Santé Numérique Rouen-Nice) Consortium’s Journey Mots clés digital health medical informatics education health education curriculum students teaching materials hybrid learning program development capacity building access learning telemedicine training medical simulation objective structured clinical examination OSCE Domaines Informatique [cs] Environnements Informatiques pour...
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Understanding metric-related pitfalls in image analysis validation - Nature Methods

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By Annika Reinke, Minu Tizabi, Michael Baumgartner Verified, A. Emre Kavur, Tim Rädsch, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Jianxu Chen, Beth Cimini, Ben Glocker, Daniel A. Hashimoto, Michael Hoffman, Pierre Jannin, Charles Kahn, Alexandros Karargyris, Jens Kleesiek, Annette Kopp-Schneider, Michal Kozubek, Anna Kreshuk, Bennett A. Landman, Geert Litjens, Anne Martel, Erik Meijering, Bjoern Menze, Henning Ernst Müller, Susanne Rafelski, Nasir Rajpoot, Michael A. Riegler, Nicola Rieke, Julio Saez-Rodriguez, Ronald M. Summers, Aleksei Tiulpin, Gael Varoquaux, Paul Jäger, Patrick Godau, Brennan Nichyporuk, Ziv R. Yaniv
| Nature Verified
Abstract Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately.
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