# Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering Source: [Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:33:04.779Z Authors: Cuong Nhat Ha, Shima Asaadi, Sanjeev Kumar Karn, Oladimeji Farri, Tobias Heimann, Thomas Runkler Published: 2024-04-24T20:31:15.000Z ## Abstract Vision-language models, while effective in general domains and showing strong performance in diverse multi-modal applications like visual question-answering (VQA), struggle to maintain the same level of effectiveness in more specialized domains, e.g., medical. We propose a medical vision-language model that integrates large vision and language models adapted for the medical domain. This model goes through three stages of parameter-efficient training using three separate biomedical and radiology multi-modal visual and text datasets. The proposed model achieves state-of-the-art performance on the SLAKE 1.0 medical VQA (MedVQA) dataset with an overall accuracy of 87.5% and demonstrates strong performance on another MedVQA dataset, VQA-RAD, achieving an overall accuracy of 73.2%. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()