Deep learning based multimodal biomedical data fusion: an overview and comparative review
Integrity signals
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Claims the paper makes
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Duan et al. provided a detailed review of multimodal learning methods for different biomedical data modalities potentially applicable in clinical scenarios.
“Duan et al. provided detailed review of multimodal learning methods for different biomedical data modalities potentially applicable in clinical scenarios [32].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [32]
Multimodal data harmonization is particularly challenging because differences in data formats, medical devices, variable definitions, and measurement units across centers make it difficult to effectively combine and analyze diverse datasets for robust model development and validation.
“Multimodal data harmonization is particularly challenging, as differences in data formats, medical devices, variable definitions, and measurement units across centers make it difficult to effectively combine and analyze diverse datasets for robust model development and validation [8,107].”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [107]
The citation lists haven’t been fetched yet
References 163 · Cited by 284
- Authors
- Duan, J., Xiong, J., Li, Y., & Ding, W.
- Affiliations
- City University of Macau (MO)
- Jinan University (CN)
- Nantong University (CN)
- Corresponding author
- Ding, Weiping
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 112
- Pages
- 102536
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W4400102178
- Cited by
- 284
- References
- 163
- Citations by year
- 2026: 114 · 2025: 151 · 2024: 19
- FWCI
- 39.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Fusion (publishedVersion)
- Funders
- National Natural Science Foundation of China (T2341004)
- Fundamental Research Funds for the Central Universities (11623201)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- multimodal fusion, deep learning, fusion levels, biomedical datasets, generative models, large language models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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