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    Meta shares 10 manuscripts from human-led, AI-assisted health research

    Meta says the manuscripts span cardiology, oncology, infectious disease and emergency medicine, with experts retaining scientific control.

    Jason WeiJW
    Alexandr WangAW
    Xin Eric Wang @ COLM 2026XE
    32 Sources, 4h ago, first seen 4h ago

    TLDR

    Meta says its human-led research scaffold used Muse Spark 1.3 and Muse Wonder, with access to 5 million retrospective, de-identified electronic health records and public data. A Meta researcher says a small group of medical experts produced the 10 manuscripts in around two weeks and found the setup helped them test hypotheses more quickly. Meta says the experts retained authority over scientific decisions.

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    32 Sources, first seen 4h ago

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    Combined views

    73K

    32 Sources, first seen 4h ago

    852 likes80 comments194 saves125 reposts
    Image: @AIatMeta on X

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    32 Sources

    Jason Wei@_jasonweiIn 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 ever4h
    ϽΓΣⱤẛ∁@CholericClericAll healthcare research people, in fact, all science people, pay attention. I have experienced this benefit with several AI tools — the ability to bounce ideas, brainstorm, develop hypotheses, and reject many. I am yet to test Muse Wonder. Now I will.4h
    Arturo LoAIza-Bonilla, MD MSEd@DrArturoAIThank you @_jasonwei @alexandr_wang @finkd and the researchers at @Meta to let us dive deep in the Muse Wonder models and generate scientific insights!! Very proud of the team!! 🚀 @mystlukes4h
    Sam Ashoo@EMedPhysSuch a pleasure to be part of this team! Looking forward to what more we can accomplish!4h
    Kritika Singh, PhD@kritika_singh24Thrilled to have worked with this team! I really enjoyed having the Muse Wonder scaffold as a collaborator to bounce ideas off of. It reduced the admin overhead of research, sped up my workflow from idea to manuscript, and taught me a lot along the way. Super proud of what we put together, and excited to keep exploring how AI can support scientific research!3h
    Ashish Shenoy@_memoryleakExcited to share some work from our health team at Meta! We set out to answer what happens when you combine medical experts, a strong coding scaffold (Muse Wonder) and 5M licensed de-identified EHR? This helped us explore the breadth of how much AI could accelerate health science3h
    Grégoire Mialon@mialon_gregoireThe ambition is contagious. The prognosis looks good3h
    Linda Gong@lindugongHealth is one of the domains where AI contributions will be undeniably positive. So much of the human body isn’t yet understood and so much of health is deeply personal. I’m hopeful that within this paradigm we’ll achieve scientific milestones like a generalized disease model bring that to you via hyperpersonalized analysis and recommendations. Our work here is a first step. Congrats @_jasonwei and team!3h
    Prof. Wayne Xu@WeiaiWayne2026-27 might be the years ai agents truly break out in scientific discovery (it's been brewing for a while), but they might be finally gaining mainstream legitimacy and public awareness. Soon, social scientists will face the same choices, shocks, and dilemmas that STEM fields are currently dealing with. So, buckle up!3h
    Paul Javid@pauljavid👊@_jasonwei accelerating the frontier for health research, and championing health within meta.2h
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    32 Sources

    Jason Wei@_jasonweiIn 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 ever4h
    ϽΓΣⱤẛ∁@CholericClericAll healthcare research people, in fact, all science people, pay attention. I have experienced this benefit with several AI tools — the ability to bounce ideas, brainstorm, develop hypotheses, and reject many. I am yet to test Muse Wonder. Now I will.4h
    Arturo LoAIza-Bonilla, MD MSEd@DrArturoAIThank you @_jasonwei @alexandr_wang @finkd and the researchers at @Meta to let us dive deep in the Muse Wonder models and generate scientific insights!! Very proud of the team!! 🚀 @mystlukes4h
    Sam Ashoo@EMedPhysSuch a pleasure to be part of this team! Looking forward to what more we can accomplish!4h
    Kritika Singh, PhD@kritika_singh24Thrilled to have worked with this team! I really enjoyed having the Muse Wonder scaffold as a collaborator to bounce ideas off of. It reduced the admin overhead of research, sped up my workflow from idea to manuscript, and taught me a lot along the way. Super proud of what we put together, and excited to keep exploring how AI can support scientific research!3h
    Ashish Shenoy@_memoryleakExcited to share some work from our health team at Meta! We set out to answer what happens when you combine medical experts, a strong coding scaffold (Muse Wonder) and 5M licensed de-identified EHR? This helped us explore the breadth of how much AI could accelerate health science3h
    Grégoire Mialon@mialon_gregoireThe ambition is contagious. The prognosis looks good3h
    Linda Gong@lindugongHealth is one of the domains where AI contributions will be undeniably positive. So much of the human body isn’t yet understood and so much of health is deeply personal. I’m hopeful that within this paradigm we’ll achieve scientific milestones like a generalized disease model bring that to you via hyperpersonalized analysis and recommendations. Our work here is a first step. Congrats @_jasonwei and team!3h
    Prof. Wayne Xu@WeiaiWayne2026-27 might be the years ai agents truly break out in scientific discovery (it's been brewing for a while), but they might be finally gaining mainstream legitimacy and public awareness. Soon, social scientists will face the same choices, shocks, and dilemmas that STEM fields are currently dealing with. So, buckle up!3h
    Paul Javid@pauljavid👊@_jasonwei accelerating the frontier for health research, and championing health within meta.2h
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