Medical question answering: A comprehensive multimodal and LLM-driven survey

Medical Question Answering (MQA) has emerged as a critical artificial intelligence (AI) capability for supporting clinicians, researchers, and the general public with timely and evidence-based responses to medical queries. Recent advances in natural language processing (NLP), computer vision, and large language models (LLMs) have expanded MQA from text-only systems to multimodal frameworks. This survey aims to provide a comprehensive and structured review of MQA systems, covering both text and image-based approaches. We present a systematic review of MQA literature, including applications, datasets, and modeling paradigms. We introduce a unified taxonomy categorizing MQA systems into scientific, clinical, consumer, and examination-oriented tasks. We also analyze representative datasets for text-based and vision-based question answering, focusing on data sources, annotation strategies, task formulations, and evaluation protocols. Furthermore, we review methodological developments ranging from classical and transformer-based models to multimodal vision-language systems and LLM-driven approaches. The analysis highlights a rapid evolution of MQA systems toward multimodal and LLM-based frameworks, particularly in medical visual question answering. Existing datasets and models demonstrate strong progress but also reveal limitations in generalization, reasoning, and real-world clinical applicability. Key challenges remain, including reliability, hallucination, explainability, fairness, and clinical safety. This survey identifies open research directions such as improved data quality, knowledge-grounded reasoning, trustworthy evaluation, and real-world deployment. The study provides a comprehensive reference and roadmap for developing reliable and clinically applicable MQA systems.