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Shashir Reddy

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Articles

An open vision-language model for diverse medical applications

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By Sahar Kazemzadeh, Fereshteh Mahvar, Atilla P. Kiraly, Timo Kohlberger, Chufan Gao, Ronnachai Jaroensri, Shawn Xu, Cian O Hughes, Cían Hughes, Charles T. Lau, Justin Chen, Tiffany Chen, Jeremy Lai, Samuel Schmidgall, Lu Yang, Kejia Chen, Kenneth A Philbrick, Mercy Asaa Asiedu, Howard Hu, Howard Yang, Richa Tiwari, Sunny Jansen, Preeti Singh, Yubin Kim, Daniel McDuff, Catherine Kozlowski, Shreya pathak, Nino Vieillard, Sarah Perrin, Tatiana Matejovicova, Thomas Mesnard, Geoffrey Cideron, Michelle Casbon, Jean-Baptiste Alayrac, Vlad Feinberg, Sebastian Borgeaud, Cassidy Hardin, Robert Dadashi, Léonard Hussenot, Armand Joulin, Clement Farabet, Joelle Barral, Tris Warkentin, Jonathon Shlens, David Fleet Verified, Shekoofeh Azizi, Omar Sanseviero, Gus Martins, Phoebe Kirk, Anand Rao, Yossi Matias, Katherine Chou, Avinatan Hassidim, Dale Webster, Shravya Shetty, Yun Liu, David Steiner, Rory Pilgrim, Daniel Golden Verified, Lin Yang, Andrew Sellergren, Madeleine Traverse, Fayaz Jamil, Liron Yatziv, Bram Sterling, Stefanie Anna Baby, Susanna Maria Baby, Per Bjornsson, Shashir Reddy, Ryan Brush, Bashir Sadjad, Ines Mezerreg, Rachelle Sico, Aishwarya Kamath, Johan Ferret, Ramona Merhej, Alexandre Ramé, Morgane Riviere, Louis Rouillard, Jean-bastien Grill, Sabela Ramos, Edouard Yvinec, Elena Buchatskaya, Dmitry Lepikhin, Alek Andreev, Olivier Bachem, Victor Cotruta, Kavi Goel, Can Kirmizibayrak
| Nature Verified
Abstract Artificial intelligence has high potential for impact in healthcare applications, but its training and deployment are challenging due to diverse data, a complex spectrum of possible tasks and important privacy needs. High-performing foundation models that enable data-efficient fine-tuning for diverse downstream tasks can meaningfully accelerate development in this domain. We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3.
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