Meta 构建了由 Muse Spark 1.3 和 Muse Wonder 驱动的人类主导研究框架,接入 500 万份回顾性去标识电子健康记录及公开数据,由医学专家团队使用该系统开展原创研究,加速假设生成、代码执行和论文撰写,同时保留对科学决策的完全控制权。目前已分享 10 篇由此产出的研究论文,覆盖心脏病学、肿瘤学、传染病、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