Academic Journal
Medical question answering: A comprehensive multimodal and LLM-driven survey.
| Τίτλος: | Medical question answering: A comprehensive multimodal and LLM-driven survey. |
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| Συγγραφείς: | 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. |
| Πηγή: | Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2026 Oct; Vol. 285, pp. 109541. Date of Electronic Publication: 2026 Jul 06. |
| Τύπος έκδοσης: | Journal Article; Systematic Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | 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- |
| Ιατρικοί όροι (MeSH): | Natural Language Processing* , Artificial Intelligence*, Large Language Models ; Humans ; Surveys and Questionnaires |
| Περίληψη: | 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 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1872-7565 |
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| DOI: | 10.1016/j.cmpb.2026.109541 |