Meta 探索用 AI 加速健康科学研究,构建了由 Muse Spark 1.3 和 Muse Wonder 驱动的人类主导研究框架,可访问 500 万份回顾性去标识电子健康记录及公开数据。医学专家团队用该系统生成假设、执行代码并起草论文,同时保留对科学决策的完全主导权。Meta 现分享由此产出的 10 篇研究论文,涵盖心脏病学、肿瘤学、传染病和急诊医学等领域。
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, emergency medicine, and more.
Read more in our research blog: https://go.meta.me/3U06wvD
来源:AI at Meta · x.com