Papers by Galina Zubkova

4 papers
MADD: Multi-Agent Drug Discovery Orchestra (2025.findings-emnlp)

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Challenge: Recent advances in artificial intelligence have limited access to wet-lab tools for hit identification . multi-agent systems combine interpretability of LLMs with precision of specialized models and tools .
Approach: They propose a multi-agent system that builds and executes customized hit identification pipelines from natural language queries.
Outcome: The proposed system reduces the complexity of traditional screening methods and improves efficiency.
RuCCoD: Towards Automated ICD Coding in Russian (2025.emnlp-main)

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Challenge: a new dataset for clinical coding in Russian is available for download . human coders must navigate a wide array of medical terminology and time pressures .
Approach: They present a new dataset for ICD coding in Russian, a language with limited biomedical resources.
Outcome: The proposed model improves accuracy on an in-house EHR dataset from 2017 to 2021.
3MDBench: Medical Multimodal Multi-agent Dialogue Benchmark (2025.emnlp-main)

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Challenge: Large Vision-Language Models (LVLMs) are being explored in medicine but their ability to conduct complex real-world telemedicine consultations remains underexplored.
Approach: They propose to use large vision-language models to conduct telemedicine consultations using a framework that simulates patient variability and evaluates diagnostic accuracy and dialogue quality via Assessor Agent.
Outcome: The proposed framework compares diagnostic strategies for open and closed-source LVLMs and shows that multimodal dialogue improves F1 score by 6.5% over non-dialogue settings.
RuCCoN: Clinical Concept Normalization in Russian (2022.findings-acl)

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Challenge: a new dataset for clinical concept normalization in Russian is available for download . ehrs contains over 16,028 entity mentions manually linked to over 2,409 unique concepts .
Approach: They present a dataset for clinical concept normalization in Russian manually annotated by medical professionals.
Outcome: The proposed dataset contains 16,028 entity mentions manually linked to over 2,409 unique concepts from the Russian language part of the UMLS ontology.

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