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

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Title: Medical question answering: A comprehensive multimodal and LLM-driven survey.
Authors: Mhedhbi E; Declic AI Research, Riyadh, Saudi Arabia. Electronic address: mhedhbiaya99@gmail.com., Zhu X; Laboratoire Images, Signaux et Systèmes Intelligents (LISSI) EA 3956, Université Paris-Est Créteil (UPEC), 122 rue Paul Armangot Vitry-sur-Seine, 94400, Paris, France., Ayaz M; Department of Applied Art and Technology, Chung-Ang University, Anseong 17546, South Korea., Khan M; College of Information Technology, United Arab Emirates University (UAEU), Al Ain, United Arab Emirates. Electronic address: mustaqeemkhan@uaeu.ac.ae., Othmani A; Declic AI Research, Riyadh, Saudi Arabia; Laboratoire Images, Signaux et Systèmes Intelligents (LISSI) EA 3956, Université Paris-Est Créteil (UPEC), 122 rue Paul Armangot Vitry-sur-Seine, 94400, Paris, France.
Source: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2026 Oct; Vol. 285, pp. 109541. Date of Electronic Publication: 2026 Jul 06.
Publication Type: Journal Article; Systematic Review
Language: English
Journal Info: Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7565 (Electronic) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE
Imprint Name(s): Publication: Limerick : Elsevier Scientific Publishers
Original Publication: Amsterdam : Elsevier Science Publishers, c1984-
MeSH Terms: Natural Language Processing* , Artificial Intelligence*, Large Language Models ; Humans ; Surveys and Questionnaires
Abstract: 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.
(Copyright © 2026 Elsevier B.V. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Artificial intelligence; Large language models; Medical question answering; Natural language processing
Entry Date(s): Date Created: 20260717 Date Completed: 20260806 Latest Revision: 20260828
Update Code: 20260828
DOI: 10.1016/j.cmpb.2026.109541
PMID: 42468440
Database: MEDLINE
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  Data: Medical question answering: A comprehensive multimodal and LLM-driven survey.
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  Data: <searchLink fieldCode="AU" term="%22Mhedhbi+E%22">Mhedhbi E</searchLink>; Declic AI Research, Riyadh, Saudi Arabia. Electronic address: mhedhbiaya99@gmail.com.<br /><searchLink fieldCode="AU" term="%22Zhu+X%22">Zhu X</searchLink>; Laboratoire Images, Signaux et Systèmes Intelligents (LISSI) EA 3956, Université Paris-Est Créteil (UPEC), 122 rue Paul Armangot Vitry-sur-Seine, 94400, Paris, France.<br /><searchLink fieldCode="AU" term="%22Ayaz+M%22">Ayaz M</searchLink>; Department of Applied Art and Technology, Chung-Ang University, Anseong 17546, South Korea.<br /><searchLink fieldCode="AU" term="%22Khan+M%22">Khan M</searchLink>; College of Information Technology, United Arab Emirates University (UAEU), Al Ain, United Arab Emirates. Electronic address: mustaqeemkhan@uaeu.ac.ae.<br /><searchLink fieldCode="AU" term="%22Othmani+A%22">Othmani A</searchLink>; Declic AI Research, Riyadh, Saudi Arabia; Laboratoire Images, Signaux et Systèmes Intelligents (LISSI) EA 3956, Université Paris-Est Créteil (UPEC), 122 rue Paul Armangot Vitry-sur-Seine, 94400, Paris, France.
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  Data: 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.<br /> (Copyright © 2026 Elsevier B.V. All rights reserved.)
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