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In the healthcare domain, summarizing medical questions posed by patients iscritical for improving doctor-patient interactions and medical decision-making.Although medical data has grown in complexity and quantity, the current body ofresearch in this domain has primarily concentrated on text-based methods,overlooking the integration of visual cues. Also prior works in the area ofmedical question summarisation have been limited to the English language. Thiswork introduces the task of multimodal medical question summarization forcodemixed input in a low-resource setting. To address this gap, we introducethe Multimodal Medical Codemixed Question Summarization MMCQS dataset, whichcombines Hindi-English codemixed medical queries with visual aids. Thisintegration enriches the representation of a patient's medical condition,providing a more comprehensive perspective. We also propose a framework namedMedSumm that leverages the power of LLMs and VLMs for this task. By utilizingour MMCQS dataset, we demonstrate the value of integrating visual informationfrom images to improve the creation of medically detailed summaries. Thismultimodal strategy not only improves healthcare decision-making but alsopromotes a deeper comprehension of patient queries, paving the way for futureexploration in personalized and responsive medical care. Our dataset, code, andpre-trained models will be made publicly available.

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