Meta 健康团队用 Muse Wonder 帮医学专家两周写出 10 篇论文
Meta 团队拿 Muse Wonder 加 500 万份病历，让医生两周写了 10 篇论文稿，还聊了人机怎么分工做科研。
Meta 健康工作stream用 Muse Wonder 编程脚手架，接入含 500 万份电子健康记录的授权数据库，让一组医学专家用 AI 做流行病学研究。专家在约两周内产出 10 份论文手稿，其中 Paddy Ssentongo 一人完成 3 份。项目测试的问题包括“服用他汀类药物的人糖尿病发病率是否更高”这类假设。团队认为解读噪声数据和判断关键变量仍需人类研究者，AI 的角色是辅助人类主导的研究。
In our health workstream at Meta we did a project where we wanted to see what research papers medical experts could write using a Muse Wonder coding scaffold that could access a database of 5M electronic health records we licensed from a vendor. The goal is that with 5M patients in this database, AI could do research to answer arbitrary questions like “did people who take statin have higher rates of diabetes later in life”? The thing we were betting on here is on what happens when you put human experts, a strong coding model, and access to data all together
We worked with a small group of medical experts to help them use this AI tool and the punchline is that they all found that this setup enabled a much faster pace of health research. They generated ten manuscripts in around two weeks, with one researcher (Paddy Ssentongo) generating three. Throughout the process, they collaborated with each other to review manuscripts and write skill files to help our coding scaffold improve at doing research. The medical experts liked how quickly they could test different hypotheses and choose the best ones to continue working on
Health research is pretty unique in that it is super important to be able to interpret noisy data and have taste to know which variables are important, which was still a significant value add from human researchers. Given that medicine is one of the oldest fields in human history and it is ultimately about humans, keeping a substantial human component in this type of research is probably important. In the near future I see AI primarily as a tool to facilitate human-led research. With the right scaffold I believe health researchers can be 10x or even 100x more productive using AI tools that have appropriate access to the wealth of data that the world has collected about human health
One way to see this work is through the broader lens of AI for science. You can think of science as two parts: (1) collecting raw data about the world, and (2) synthesizing that raw data into understanding. In this project we already had existing data about the world and we aimed to accelerate part (2) using AI. Eventually, I imagine a world where we have such great AI tools that the time to synthesize raw data into understanding is driven towards zero. In that world, science will be substantially faster and most of the work will be focused on collecting the right raw data via physical experiments
We did this in a pretty short amount of time (which was the point) and I think the future of scientific research will be more faster and more exciting than ever
