MedSumm: A Multimodal Approach to Summarizing Code-Mixed Hindi-English Clinical Queries

• URL: http://arxiv.org/abs/2401.01596v1
• Date: Wed, 3 Jan 2024 07:58:25 GMT
• Title: MedSumm: A Multimodal Approach to Summarizing Code-Mixed Hindi-English Clinical Queries
• Authors: Akash Ghosh, Arkadeep Acharya, Prince Jha, Aniket Gaudgaul, Rajdeep Majumdar, Sriparna Saha, Aman Chadha, Raghav Jain, Setu Sinha, and Shivani Agarwal
• Abstract summary: We introduce the Multimodal Medical Codemixed Question Summarization MMCQS dataset. This dataset combines Hindi-English codemixed medical queries with visual aids. Our dataset, code, and pre-trained models will be made publicly available.
• Score: 16.101969130235055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the healthcare domain, summarizing medical questions posed by patients is critical for improving doctor-patient interactions and medical decision-making. Although medical data has grown in complexity and quantity, the current body of research in this domain has primarily concentrated on text-based methods, overlooking the integration of visual cues. Also prior works in the area of medical question summarisation have been limited to the English language. This work introduces the task of multimodal medical question summarization for codemixed input in a low-resource setting. To address this gap, we introduce the Multimodal Medical Codemixed Question Summarization MMCQS dataset, which combines Hindi-English codemixed medical queries with visual aids. This integration enriches the representation of a patient's medical condition, providing a more comprehensive perspective. We also propose a framework named MedSumm that leverages the power of LLMs and VLMs for this task. By utilizing our MMCQS dataset, we demonstrate the value of integrating visual information from images to improve the creation of medically detailed summaries. This multimodal strategy not only improves healthcare decision-making but also promotes a deeper comprehension of patient queries, paving the way for future exploration in personalized and responsive medical care. Our dataset, code, and pre-trained models will be made publicly available.

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