Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI

Authors: Rotem Gershon , Seffi Cohen , Dean Ariel , Havatzelet Bilavsky-Yarden , Amiel Sberro , Shai Yitzhaki , Osnat Tausky , Naama Golan , Maayan Rabinovitch , Ariel Avraham Hasidim , Itay Arianu , Maya Dagan , Ran Balicer , Noa Dagan

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Abstract: Automated judges of clinical artificial intelligence (AI) outputs are necessary, but large language model (LLM) judges are costly and slow at scale. We compared Jev, a decision language model that returns structured judgments, with frontier and open-weight LLM judges in four use cases. On 118 pediatric answers, Jev's agreement with physicians' ordinal quality ratings (Gwet's AC2 0.50; expected score 0.53) matched or exceeded that of seven of 12 LLM judges, including frontier models, Claude Opus 5.5 (0.49) and GPT-6 (0.38). On physician-graded binary HealthBench criteria, Jev matched or exceeded four of six LLM judges. On MedHELM's open-ended tasks, Jev's agreement with a three-LLM jury approached or exceeded the jurors' internal agreement. On HealthBench Professional, Jev matched the benchmark's reference judge, GPT-5.4, on 91% of criteria (other judges, 91-93%). Frontier judges cost 73-294 times more than Jev and took 7-57 times longer. Jev may enable efficient clinical AI evaluation at scale.

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Artificial Intelligence (cs.AI)

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arXiv:2610.28826 [cs.AI]

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arXiv:2610.28826v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.28826

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arXiv-issued DOI via DataCite (pending registration)

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From: Seffi Cohen [ view email ]

[v1]

Tue, 20 Oct 2026 18:46:47 UTC (2,804 KB)

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