Katherine Chou on Muck Rack

Katherine Chou

New York
As seen in: MSN, Nature, The Keyword, Flipboard, Science Magazine, Town & Country, WCVB-TV (Boston, MA), WESH-TV (Orlando, FL), WBAL-TV (Baltimore, MD), WLWT-TV (Cincinnati, OH) and
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Articles

Towards Conversational AI for Disease Management

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By Valentin Liévin, Anil Palepu, Wei-Hung Weng, Khaled Saab, David Stutz, Yong Cheng, Kavita Kulkarni, S. Sara Mahdavi, Joelle Barral, Dale Webster, Katherine Chou, Avinatan Hassidim, Yossi Matias, James Manyika, Ryutaro Tanno, Vivek S. Natarajan, Adam Rodman, Tao Tu, Alan Karthikesalingam, Mike Schaekermann
| Nature Verified
Abstract While large language models (LLMs) have shown promise in diagnostic dialogue1, their capabilities for effective management reasoning—including disease progression, therapeutic response, and safe medication prescription—remain under-explored. We advance the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE)1−3 through a new LLM-based agentic system optimized for multi-visit clinical management and dialogue.
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Accelerating scientific discovery with Co-Scientist

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By Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Dan Popovici, Anil Palepu, Ryutaro Tanno, Khaled Saab, fan zhang, Jacob Blum, Andrew Carroll, Kavita Kulkarni, Nenad Tomasev, Dina Zverinski, Elahe Vedadi, Ben Feinstein, Jan Freyberg, Jeremy Ratcliff, Ottavia Bertolli, Katherine Chou, Avinatan Hassidim, Burak Gokturk, Amin Vahdat, Yuan Guan, Vikram Dhillon, Eeshit Dhaval Vaishnav, Byron Lee, Tiago Costa, José R. Penadés, Gary Peltz, Yossi Matias, James Manyika, Yunhan Xu, Pushmeet Kohli, Annalisa Pawlosky, Alan Karthikesalingam, Vivek T Natarajan, Petar Sirkovic, Artiom Myaskovsky, Grzegorz Glowaty, Felix Weissenberger, Alessio Orlandi, Keran Rong, Ivor Rendulic, Florian Hasler, Luka Rimanic, Marina Boia, Ivan Budiselic, Mathias Bellaiche, Tom Sheffer
| Nature Verified
Abstract Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge.
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An AI system to help scientists write expert-level empirical software

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By Anastasiya Belyaeva, Gheorghe Comanici, Marc A. Coram, Hao Cui, Jake Garrison, Renee Johnston, Anton Kast, Cory Y McLean, Peter Norgaard, Zahra Shamsi, James Thompson, Subhashini Venugopalan, BRIAN WILLIAMS, Chujun He, Sarah Martinson Verified, Lai Wei, Yuchen Zhou, Qian-Ze Zhu, Matthew Abraham, Erica Lorenzo Brand, Chris Co, Grace Joseph, Ryan Krueger, Xuefei Wang, Katherine Chou, James Manyika, Yossi Matias, John Platt Verified, Lizzie Dorfman, Shibl Mourad, Michael Brenner, Eser Aygün, David Smalling, Martyna Plomecka, Anna Bulanova, Jeffrey A. Cardille, Malcolm Kane, Johan Kartiwa, Dan Liebling, Jan-Matthis Lueckmann, Paul Raccuglia
| Nature Verified
Abstract The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments1. To address this, we present Empirical Research Assistance (ERA), an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS)2 to systematically improve the quality metric and intelligently navigate the large space of possible solutions.
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