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A general-purpose System One decision model for clinical records — arXiv:2610.27099

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.27099
License (as archived): CC0-1.0
Archived: 2026-10-21T07:06:02.139Z
SHA-256 of the archived text: e41c9216038cea71791a8d9d68a2a5a0a29864bf00462eb2d916b55b6f13530c
Stable URL of this page: https://metraipolis.com/documento/dc_3b7c19c0404d

How to cite

A general-purpose System One decision model for clinical records — arXiv:2610.27099 — arXiv (en), https://arxiv.org/abs/2610.27099 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3b7c19c0404d (Metraipolis archive dc_3b7c19c0404d, sha256 e41c9216038cea71791a8d9d68a2a5a0a29864bf00462eb2d916b55b6f13530c)

BibTeX

@misc{dc3b7c19c0404d,
  title = {A general-purpose System One decision model for clinical records — arXiv:2610.27099},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.27099},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3b7c19c0404d; sha256 e41c9216038cea71791a8d9d68a2a5a0a29864bf00462eb2d916b55b6f13530c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.27099. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Clinical care, health-system operations and biomedical discovery depend on extracting reliable answers from patient records, yet applying diverse clinical questions across entire populations remains computationally expensive. Here we present Clinical System One (CS1), a 4-billion-parameter decision model for fast, typed judgments from clinical records, along with two agentic harnesses in which CS1 works together with a generalist language model. CS1-4B is trained on 12.4 million questions and answers from 3.6 million patients across a multi-hospital health system, combining structured-data keys, observed outcomes and language-model supervision across clinical notes, diagnostic reports and patient timelines. It answers multiple typed questions in a single forward pass without task-specific retraining, and each answer carries a probability that can route uncertain cases to human review. On 963,408 held-out patients, CS1-4B outperformed open decision models up to 27 billion parameters on 13 of 14 tasks scored independently of its training teacher. Across 21 external data sets, its aggregate performance approached that of the strongest tested 27-billion-parameter model, with 6.4-fold faster inference in a matched workload. Integrating CS1-4B with a language model agent produced cited answers over complete multi-year patient histories and screened a patient population against the eligibility criteria of 17 registered clinical trials, placing four times as many judged-eligible patients among its top 20 candidates as keyword search while using 690-fold fewer language-model tokens than a language model reading every record. These findings support a division of labour in clinical AI, with small decision models performing repeated record reading and generative models formulating questions and synthesizing evidence.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.27099 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-21T07:05:43.379Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).