@inproceedings{kuzmin-etal-2026-uncertainty,
title = "Uncertainty Guardrails for {LLM}-Based Medical Diagnostic Systems",
author = "Kuzmin, Gleb and
Rvanova, Lyudmila and
Kopanichuk, Ilia and
Makharev, Vladimir and
Bakulin, Ivan and
Shaposhnikov, Vladimir and
Oskin, Artem and
Anokhin, Petr and
Nesterov, Aleksandr and
Vazhentsev, Artem and
Panchenko, Alexander and
Tutubalina, Elena and
Bespalov, Iaroslav Radionovich and
Dylov, Dmitry V. and
Shelmanov, Artem and
Oseledets, Ivan",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.225/",
pages = "3764--3780",
ISBN = "979-8-89176-460-6",
abstract = "Large language models (LLMs) are increasingly being adopted in clinical practice, e.g., in medical diagnostic systems. However, their reliability is still a concern, as LLMs are prone to hallucinations and could lead to misdiagnosis, inappropriate treatment decisions, and potential harm to patients. To mitigate these risks, LLM-based medical diagnostic systems should incorporate safety guardrails capable of detecting suspicious predictions and flagging them for human review or further clinical assessment. In this work, we employ uncertainty quantification (UQ) as a safety mechanism within an enterprise diagnostic system. We evaluate 16 UQ methods and propose two task-specific modifications tailored to the medical diagnostic workflow. First, we show that methods based on semantic similarity between generated samples substantially benefit from replacing a general-purpose NLI model with a domain-specific model. Second, we adapt a supervised UQ method that relies on LLM internal states for the detection of erroneous generation from a black-box enterprise LLM accessible only through a text-generation API. Our approach leverages the internal states of an open-source, lightweight proxy white-box model. We demonstrate that this approach outperforms all other UQ methods in the considered setting."
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<abstract>Large language models (LLMs) are increasingly being adopted in clinical practice, e.g., in medical diagnostic systems. However, their reliability is still a concern, as LLMs are prone to hallucinations and could lead to misdiagnosis, inappropriate treatment decisions, and potential harm to patients. To mitigate these risks, LLM-based medical diagnostic systems should incorporate safety guardrails capable of detecting suspicious predictions and flagging them for human review or further clinical assessment. In this work, we employ uncertainty quantification (UQ) as a safety mechanism within an enterprise diagnostic system. We evaluate 16 UQ methods and propose two task-specific modifications tailored to the medical diagnostic workflow. First, we show that methods based on semantic similarity between generated samples substantially benefit from replacing a general-purpose NLI model with a domain-specific model. Second, we adapt a supervised UQ method that relies on LLM internal states for the detection of erroneous generation from a black-box enterprise LLM accessible only through a text-generation API. Our approach leverages the internal states of an open-source, lightweight proxy white-box model. We demonstrate that this approach outperforms all other UQ methods in the considered setting.</abstract>
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%0 Conference Proceedings
%T Uncertainty Guardrails for LLM-Based Medical Diagnostic Systems
%A Kuzmin, Gleb
%A Rvanova, Lyudmila
%A Kopanichuk, Ilia
%A Makharev, Vladimir
%A Bakulin, Ivan
%A Shaposhnikov, Vladimir
%A Oskin, Artem
%A Anokhin, Petr
%A Nesterov, Aleksandr
%A Vazhentsev, Artem
%A Panchenko, Alexander
%A Tutubalina, Elena
%A Bespalov, Iaroslav Radionovich
%A Dylov, Dmitry V.
%A Shelmanov, Artem
%A Oseledets, Ivan
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F kuzmin-etal-2026-uncertainty
%X Large language models (LLMs) are increasingly being adopted in clinical practice, e.g., in medical diagnostic systems. However, their reliability is still a concern, as LLMs are prone to hallucinations and could lead to misdiagnosis, inappropriate treatment decisions, and potential harm to patients. To mitigate these risks, LLM-based medical diagnostic systems should incorporate safety guardrails capable of detecting suspicious predictions and flagging them for human review or further clinical assessment. In this work, we employ uncertainty quantification (UQ) as a safety mechanism within an enterprise diagnostic system. We evaluate 16 UQ methods and propose two task-specific modifications tailored to the medical diagnostic workflow. First, we show that methods based on semantic similarity between generated samples substantially benefit from replacing a general-purpose NLI model with a domain-specific model. Second, we adapt a supervised UQ method that relies on LLM internal states for the detection of erroneous generation from a black-box enterprise LLM accessible only through a text-generation API. Our approach leverages the internal states of an open-source, lightweight proxy white-box model. We demonstrate that this approach outperforms all other UQ methods in the considered setting.
%U https://aclanthology.org/2026.emnlp-industry.225/
%P 3764-3780
Markdown (Informal)
[Uncertainty Guardrails for LLM-Based Medical Diagnostic Systems](https://aclanthology.org/2026.emnlp-industry.225/) (Kuzmin et al., EMNLP 2026)
ACL
- Gleb Kuzmin, Lyudmila Rvanova, Ilia Kopanichuk, Vladimir Makharev, Ivan Bakulin, Vladimir Shaposhnikov, Artem Oskin, Petr Anokhin, Aleksandr Nesterov, Artem Vazhentsev, Alexander Panchenko, Elena Tutubalina, Iaroslav Radionovich Bespalov, Dmitry V. Dylov, Artem Shelmanov, and Ivan Oseledets. 2026. Uncertainty Guardrails for LLM-Based Medical Diagnostic Systems. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 3764–3780, Budapest, Hungary. Association for Computational Linguistics.