Accelerating Scientific Research in Health Data Science With Muse Models

We’re exploring how AI can accelerate health science research. While AI has made significant breakthroughs in formal sciences like mathematics, health data science depends on imperfect real-world data and consequential choices about cohorts, variables, and causal assumptions. To explore how AI can assist health researchers, we built a human-led research scaffold powered by Muse Spark 1.3 and Muse Wonder with access to 5M retrospective de-identified electronic health records and publicly available data. A cohort of medical experts used the system to conduct original research, accelerate hypothesis generation, code execution, and manuscript drafting while maintaining full authority over scientific decisions. Today, we’re sharing 10 research manuscripts produced through this exploration, spanning cardiology, oncology, infectious disease, and emergency medicine. Read more in our research blog: https://go.meta.me/3U06wvD -- Subscribe: https://www.youtube.com/aiatmeta?sub_confirmation=1 Learn more about our work: https://ai.meta.com Follow us on Twitter: https://twitter.com/aiatmeta Follow us on Facebook: https://www.facebook.com/aiatmeta Connect with us on LinkedIn: https://www.linkedin.com/showcase/aiatmeta/ Meta focuses on bringing the world together by advancing AI, powering meaningful and safe experiences, and conducting open research.

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