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

arXiv: https://arxiv.org/abs/2401.01596 · HTML: https://mlsanity.com/paper/2401.01596/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2001.04063/) (2020)
- [XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models](https://mlsanity.com/paper/2306.07971/) (2023)
- [Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages](https://mlsanity.com/paper/2303.13592/) (2023)
- [CLIPSyntel: CLIP and LLM Synergy for Multimodal Question Summarization in Healthcare](https://mlsanity.com/paper/2312.11541/) (2023)
- [Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning](https://mlsanity.com/paper/2108.05067/) (2021)
- [Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards](https://mlsanity.com/paper/2107.00176/) (2021)
- [SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model](https://mlsanity.com/paper/2304.10691/) (2023)
- [An Investigation of Evaluation Metrics for Automated Medical Note Generation](https://mlsanity.com/paper/2305.17364/) (2023)

## Built on by (most cited first, 19 of 19)

- [A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models](https://mlsanity.com/paper/2405.09589/) (2024)
- [Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions](https://mlsanity.com/paper/2404.07214/) (2024)
- [Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding](https://mlsanity.com/paper/2505.16652/) (2025)
- [A Survey of Multilingual Reasoning in Language Models](https://mlsanity.com/paper/2502.09457/) (2025)
- [MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention](https://mlsanity.com/paper/2406.05344/) (2024)
- [SANSKRITI: A Comprehensive Benchmark for Evaluating Language Models' Knowledge of Indian Culture](https://mlsanity.com/paper/2506.15355/) (2025)
- [DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture](https://mlsanity.com/paper/2509.19274/) (2025)
- [ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos](https://mlsanity.com/paper/2405.20628/) (2024)
- [CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcare](https://mlsanity.com/paper/2512.11437/) (2025)
- [From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice](https://mlsanity.com/paper/2410.01812/) (2024)
- [Bhaasha, Bhasa, Zaban: A Survey for Low-Resourced Languages in South Asia -- Current Stage and Challenges](https://mlsanity.com/paper/2509.11570/) (2025)
- [M3Retrieve: Benchmarking Multimodal Retrieval for Medicine](https://mlsanity.com/paper/2510.06888/) (2025)
- [CarePilot: A Multi-Agent Framework for Long-Horizon Computer Task Automation in Healthcare](https://mlsanity.com/paper/2603.24157/) (2026)
- [Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development](https://mlsanity.com/paper/2405.15766/) (2024)
- [Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5](https://mlsanity.com/paper/2409.05401/) (2024)
- [GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance](https://mlsanity.com/paper/2505.04284/) (2025)
- [When Background Matters: Breaking Medical Vision Language Models by Transferable Attack](https://mlsanity.com/paper/2604.17318/) (2026)
- [M3 QuestionIng: Multi-modal Multi-span Medical Question Answering](https://mlsanity.com/paper/2606.28329/) (2026)
- [MMTClinic: Multimodal, Multilingual Time Series Question Answering and Reasoning Benchmark for Clinical Domain](https://mlsanity.com/paper/2609.04842/) (2026)

## Abstract (arXiv)

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.