Emerging trends in multi-modal artificial intelligence for clinical decision support: a narrative review
Open accessAbstract
Multimodal artificial intelligence (MMAI) integrates and interprets diverse data types, such as images, text, video, and audio, and offers new opportunities for clinical decision support systems (CDSSs). Traditional CDSSs rely on unimodal data, which limits their predictive accuracy and coverage. The incorporation of MMAI holds promise for more accurate diagnosis, treatment optimization, and personalized patients care by synthesizing heterogeneous data sources. This narrative review explores the growing role of MMAI in improving diagnostic sensitivity, personalizing treatment, and improving healthcare delivery through the integration of heterogeneous data sources. It examines the evolution of MMAI technologies, such as large language models, large vision models, vision-language models, and large multimodal models, and their practical applications in clinical settings. The review also addresses key ethical, technical, and infrastructure challenges, such as data quality, model interpretability, bias, and system interoperability. Finally, it provides strategic recommendations for clinicians, researchers, and policy makers to promote responsible adoption of MMAI in healthcare. While recent developments show significant promise, addressing current limitations is essential to fully realize the transformative potential of MMAI in modern medicine.
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Multimodal approaches have improved performance by integrating different data types.
“Multimodal approaches have improved performance by integrating different data types [5-7].”
From Medical AI Agents for Clinical Decision Support: Viewpoint Using the Planning, Action, Reflection, and Memory (PARM) Analytical Lens (2026) · cites this paper as [5]
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- Authors
- Ardic, N., & Dinc, R.
- Affiliations
- Department of Research and Development, Med-International UK Health Agency Ltd, Leicestershire, UK
- Department of Research and Development, INVAMED Medical Innovation Institute, New York, NY, USA
- ORCID
- Dinc, Rasit
- Venue
- Health Informatics Journal
- Publisher
- SAGE Publications
- Published
- 2025-07
- Volume
- 31
- Issue
- 3
- Pages
- 14604582251366141-14604582251366141
- ISSN
- 1355-4743 (ISSN-L)
- 1460-4582
- 1741-2811
- OpenAlex
- W4413117879
- Cited by
- 32
- References
- 44
- Citations by year
- 2026: 28 · 2025: 4
- FWCI
- 1.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Health Informatics Journal (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- multimodal AI, clinical decision support systems, large language models, large vision models, vision-language models, multimodal models, heterogeneous data integration, personalized treatment, diagnostic accuracy, model interpretability, algorithmic bias, data quality, interoperability, ethical considerations, responsible AI adoption
- MeSH terms
- Artificial Intelligence, Humans, Decision Support Systems, Clinical
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