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On-premise medical AI agents for reliable clinical decision-making PRIMARY_TEXT

Nature Medicine · 2026-09-15 · no page hash: the reader saw a rendering, not the bytes · created seq 15039

Link: https://www.nature.com/articles/s41591-026-04609-x · read at 2026-09-29T00:24:00Z · held by reference: only the link, the quoted excerpts with their hashes, and the page hash when the reader had the bytes are stored

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FETCHED at 2026-09-29T00:08:21Z · https://www.nature.com/articles/s41591-026-04609-x?error=cookies_not_supported&code=9853a762-67bb-4f9f-a293-fcd76e5dac46 · seq 15124

Server hash sha256:f68dc064f4ab837220e14422d608ce373f1d23f640a841d381775031ee09b18f · 498556 bytes · text/html. The reader's record is unchanged; this is the server's finding beside it.

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Across two Medical Information Mart for Intensive Care IV (MIMIC-IV)-derived benchmarks, the agent achieved 90.04% accuracy on a seven-disease task and 83.8%...verbatim — the passage is on the page, character for character
Diagnostic behavioral consistency provided the strongest discrimination of correctness (area under the curve (AUC) = 0.860) and remained informative under st...verbatim — the passage is on the page, character for character

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QUOTE {}Across two Medical Information Mart for Intensive Care IV (MIMIC-IV)-derived benchmarks, the agent achieved 90.04% accuracy on a seven-disease task and 83.8% accuracy on a four-disease task, approaching a cloud baseline on the primary be...
QUOTE {}Diagnostic behavioral consistency provided the strongest discrimination of correctness (area under the curve (AUC) = 0.860) and remained informative under stress testing (AUC = 0.875). At a consistency threshold of 0.90, 49.4% of cases w...

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