IJCAI 2024poster0 citations
GigaPevt: Multimodal Medical Assistant
Pavel Blinov, Konstantin Egorov, Ivan Sviridov, Nikolay Ivanov, Stepan Botman, Evgeniy Tagin, Stepan Kudin, Galina Zubkova
Abstract
Building an intelligent and efficient medical assistant is still a challenging AI problem. The major limitation comes from the data modality scarceness, which reduces comprehensive patient perception. This demo paper presents GigaPevt, the first multimodal medical assistant that combines the dialog capabilities of large language models with specialized medical models. Such an approach shows immediate advantages in dialog quality and metric performance, with a 1.18% accuracy improvement in the question-answering task.
Multidisciplinary Topics and Applications: MDA: Health and medicineNatural Language Processing: NLP: Dialogue and interactive systemsComputer Vision: CV: Vision and languageComputer Vision: CV: Applications
BibTeX
@inproceedings{ijcai2024p992,
title = {GigaPevt: Multimodal Medical Assistant},
author = {Blinov, Pavel and Egorov, Konstantin and Sviridov, Ivan and Ivanov, Nikolay and Botman, Stepan and Tagin, Evgeniy and Kudin, Stepan and Zubkova, Galina and Savchenko, Andrey V.},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8614--8618},
year = {2024},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2024/992},
url = {https://doi.org/10.24963/ijcai.2024/992},
}