@inproceedings{shaposhnikov-etal-2025-clarity,
title = "{CLARITY}: Clinical Assistant for Routing, Inference, and Triage",
author = "Shaposhnikov, Vladimir and
Nesterov, Alexandr and
Kopanichuk, Ilia and
Bakulin, Ivan and
Egor, Zhelvakov and
Abramov, Ruslan and
Olegovna, Tsapieva Ekaterina and
Bespalov, Iaroslav Radionovich and
Dylov, Dmitry V. and
Oseledets, Ivan",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.127/",
pages = "1805--1821",
ISBN = "979-8-89176-333-3",
abstract = "We present CLARITY (Clinical Assistant for Routing, Inference and Triage), an AI-driven platform designed to facilitate patient-to-specialist routing, clinical consultations, and severity assessment of patient conditions. Its hybrid architecture combines a Finite State Machine (FSM) for structured dialogue flows with collaborative agents that employ Large Language Model (LLM) to analyze symptoms and prioritize referrals to appropriate specialists. Built on a modular microservices framework, CLARITY ensures safe, efficient, and robust performance, flexible and readily scalable to meet the demands of existing workflows and IT solutions in healthcare.We report integration of our clinical assistant into a large-scale national interhospital platform, with more than 55,000 content-rich userdialogues completed within the two months of deployment, 2,500 of which were expert-annotated for subsequent validation. The validation results show that CLARITY surpasses human-level performance in terms of the first-attempt routing precision, naturally requiring up to 3 times shorter duration of the consultation than with a human."
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<abstract>We present CLARITY (Clinical Assistant for Routing, Inference and Triage), an AI-driven platform designed to facilitate patient-to-specialist routing, clinical consultations, and severity assessment of patient conditions. Its hybrid architecture combines a Finite State Machine (FSM) for structured dialogue flows with collaborative agents that employ Large Language Model (LLM) to analyze symptoms and prioritize referrals to appropriate specialists. Built on a modular microservices framework, CLARITY ensures safe, efficient, and robust performance, flexible and readily scalable to meet the demands of existing workflows and IT solutions in healthcare.We report integration of our clinical assistant into a large-scale national interhospital platform, with more than 55,000 content-rich userdialogues completed within the two months of deployment, 2,500 of which were expert-annotated for subsequent validation. The validation results show that CLARITY surpasses human-level performance in terms of the first-attempt routing precision, naturally requiring up to 3 times shorter duration of the consultation than with a human.</abstract>
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%0 Conference Proceedings
%T CLARITY: Clinical Assistant for Routing, Inference, and Triage
%A Shaposhnikov, Vladimir
%A Nesterov, Alexandr
%A Kopanichuk, Ilia
%A Bakulin, Ivan
%A Egor, Zhelvakov
%A Abramov, Ruslan
%A Olegovna, Tsapieva Ekaterina
%A Bespalov, Iaroslav Radionovich
%A Dylov, Dmitry V.
%A Oseledets, Ivan
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F shaposhnikov-etal-2025-clarity
%X We present CLARITY (Clinical Assistant for Routing, Inference and Triage), an AI-driven platform designed to facilitate patient-to-specialist routing, clinical consultations, and severity assessment of patient conditions. Its hybrid architecture combines a Finite State Machine (FSM) for structured dialogue flows with collaborative agents that employ Large Language Model (LLM) to analyze symptoms and prioritize referrals to appropriate specialists. Built on a modular microservices framework, CLARITY ensures safe, efficient, and robust performance, flexible and readily scalable to meet the demands of existing workflows and IT solutions in healthcare.We report integration of our clinical assistant into a large-scale national interhospital platform, with more than 55,000 content-rich userdialogues completed within the two months of deployment, 2,500 of which were expert-annotated for subsequent validation. The validation results show that CLARITY surpasses human-level performance in terms of the first-attempt routing precision, naturally requiring up to 3 times shorter duration of the consultation than with a human.
%U https://aclanthology.org/2025.emnlp-industry.127/
%P 1805-1821
Markdown (Informal)
[CLARITY: Clinical Assistant for Routing, Inference, and Triage](https://aclanthology.org/2025.emnlp-industry.127/) (Shaposhnikov et al., EMNLP 2025)
ACL
- Vladimir Shaposhnikov, Alexandr Nesterov, Ilia Kopanichuk, Ivan Bakulin, Zhelvakov Egor, Ruslan Abramov, Tsapieva Ekaterina Olegovna, Iaroslav Radionovich Bespalov, Dmitry V. Dylov, and Ivan Oseledets. 2025. CLARITY: Clinical Assistant for Routing, Inference, and Triage. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1805–1821, Suzhou (China). Association for Computational Linguistics.