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TRUVACE RECORD VERSION
record: TRV-2026-1168
version: 2
kind: revised
reason: Direction audit: filed as a problem but describes a gain.
timestamp: 2026-10-26T06:38:42.263545Z
status: published
lens: g_space
sector: health
headline: From Answers to Agents: What Must Change Before Generative and Agentic AI Become Clinical Infrastructure in Hematology
dek: Generative artificial intelligence in hematology is entering a new phase. The dominant question, whether large language models are accurate enough for clinical decision support, is being overtaken by a harder one, as systems shift from answering questions to acting: extracting structured cases, routing them, classifying variants, and grounding recommendations in guidelines and case memory. In 2026, a hematology agent achieved roughly 83% concordance with tumor-board decisions in a prospective silent trial, with…
gain_title: A hematology AI agent reached about 83% concordance with tumor-board decisions in a prospective silent trial, with hallucinations in only 0.3%.
problem_title: (none)
trace_subject: (none)
gain_reading: A hematology AI agent reached about 83% concordance with tumor-board decisions in a prospective silent trial, with hallucinations in only 0.3%.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: Generative artificial intelligence in hematology is entering a new phase. The dominant question, whether large language models are accurate enough for clinical decision support, is being overtaken by a harder one, as systems shift from answering questions to acting: extracting structured cases, routing them, classifying variants, and grounding recommendations in guidelines and case memory.

In 2026, a hematology agent achieved roughly 83% concordance with tumor-board decisions in a prospective silent trial, with hallucinations in 0.3%, suggesting that for well-structured tasks the binding constraint is shifting from accuracy to governability. For hematologists, this reframes AI adoption as a governance and human-factors program rather than a search for a better model.
limitation: 
tag: Evidence-backed gain
key_points: Generative artificial intelligence in hematology is entering a new phase. | The dominant question, whether large language models are accurate enough for clinical decision support, is being overtaken by a harder one, as systems shift from answering questions to acting: extracting structured cases, routing them, classifying variants, and grounding recommendations in guidelines and case memory. | In 2026, a hematology agent achieved roughly 83% concordance with tumor-board decisions in a prospective silent trial, with hallucinations in 0.3%, suggesting that for well-structured tasks the binding constraint is shifting from accuracy to governability.
rundown: Generative artificial intelligence in hematology is entering a new phase. The dominant question, whether large language models are accurate enough for clinical decision support, is being overtaken by a harder one, as systems shift from answering questions to acting: extracting structured cases, routing them, classifying variants, and grounding recommendations in guidelines and case memory.

In 2026, a hematology agent achieved roughly 83% concordance with tumor-board decisions in a prospective silent trial, with hallucinations in 0.3%, suggesting that for well-structured tasks the binding constraint is shifting from accuracy to governability. Moving from experimental support to reliable infrastructure requires four changes: validating trajectories rather than answers; allocating autonomy inversely to case complexity; treating deployed models as regulated instruments under continuous surveillance; and engineering against automation bias, with data-privacy and consent safeguards throughout.
sources:
- peer_reviewed | Turkish Journal of Hematology | https://doi.org/10.4274/tjh.galenos.2026.82621 | 2026-09-21
prev: 588383241407bfcb3bd959823d26fd7aff5391ae6b3cb1eb27e6208b6ea4fda8

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8261da7ed1a650b3fcee497c6e16faba9393f6a74e50c2b1c6ae659952d59ffd

Previous version’s hash

588383241407bfcb3bd959823d26fd7aff5391ae6b3cb1eb27e6208b6ea4fda8