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Today we are thrilled to announce Clinical System One (CS1), a fast and efficient general-purpose decision model for medicine.
Read our preprint 📄: arxiv.org/abs/2610.27099
Watch the video 🎬👇
Blog: linkedin.com/pulse/reading-…
Healthcare has a scale problem. Every health system holds decades of notes, reports, and timelines. They contain answers to questions we rarely get to ask of more than a few patients at a time. Which patients could join this trial? Who missed a follow-up? What happened to everyone who took this drug? Reading all of it, for every question, has always been too slow and too expensive. That was true even with LLMs.
Thats why our team built Clinical System One (CS1). Its a small, fast decision model tailored for clinical records. Trained on 12.4 million questions and 3.6 million patients, CS1-4B beats open decision models on reading real-world patient records across diverse healthcare systems, and it runs on a single desktop GPU. We show the power of composing CS1 with traditional System 2 LLMs into agentic systems that read dense patient timelines and assess trial eligibility from a pool of a thousand candidates. Imagine trials that find every eligible patient automatically, registries that fill themselves, and care gaps surfaced across a whole population. Thats the future were building toward.
Read our preprint 📄: arxiv.org/abs/2610.27099
Watch the video 🎬👇
Blog: linkedin.com/pulse/reading-…
Healthcare has a scale problem. Every health system holds decades of notes, reports, and timelines. They contain answers to questions we rarely get to ask of more than a few patients at a time. Which patients could join this trial? Who missed a follow-up? What happened to everyone who took this drug? Reading all of it, for every question, has always been too slow and too expensive. That was true even with LLMs.
Thats why our team built Clinical System One (CS1). Its a small, fast decision model tailored for clinical records. Trained on 12.4 million questions and 3.6 million patients, CS1-4B beats open decision models on reading real-world patient records across diverse healthcare systems, and it runs on a single desktop GPU. We show the power of composing CS1 with traditional System 2 LLMs into agentic systems that read dense patient timelines and assess trial eligibility from a pool of a thousand candidates. Imagine trials that find every eligible patient automatically, registries that fill themselves, and care gaps surfaced across a whole population. Thats the future were building toward.
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