ReviewBriefings in bioinformatics2022
Multimodal deep learning for biomedical data fusion: a review.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07073430 (Application Evaluation Research on the Artificial Intelligence-assisted Support System for the Diagnosis of Colorectal Tubular Adenoma Lesions), which is not on this map. Cited by 262 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Application Evaluation Research on the Artificial Intelligence-assisted Support System for the Diagnosis of Colorectal Tubular Adenoma Lesions
Who cites it
262 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multimodal machine learning for surgical decision support in epilepsy: Current evidence and translational gaps.Epilepsia · 2026Pooled it
- Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.Journal of translational medicine · 2025Pooled it
- Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.Genome research · 2026Article
- Article
- A Systematic Review of Deep Learning and Machine Learning Applications in Longitudinal Multimodal Clinical Data.Journal of healthcare informatics research · 2026Review
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Observational
- [Advances in deep learning multimodal fusion for early diagnosis of knee osteoarthritis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Multimodal artificial intelligence in prostate cancer: integrating multiparametric MRI with clinicopathological, molecular, and functional imaging data.Abdominal radiology (New York) · 2026Review
- ProtoMM: Interpretable Prototype-Based Multimodal Model for Brain Cancer Survival Prediction.Journal of imaging informatics in medicine · 2026Article
- Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.Insights into imaging · 2026Article
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Unifying multimodal single-cell data with a mixture-of-expertsbioRxiv : the preprint server for biology · 2026Article
- Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.Statistics in medicine · 2026Article
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Navigating AI and machine learning in cancer research: an end-to-end translational framework.Journal of translational medicine · 2026Review
- Multimodal deep learning for papillary thyroid carcinoma diagnosis using ultrasound and cytology.BMC medical imaging · 2026Article
- Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.Biomedical engineering online · 2026Article
- Multimodal fusion-based prediction of postoperative survival in gallbladder adenocarcinoma: Model development and validation.Scientific reports · 2026Article
202 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships. Therefore, we review the current state-of-the-art of such methods and propose a detailed taxonomy that facilitates more informed choices of fusion strategies for biomedical applications, as well as research on novel methods. By doing so, we find that deep fusion strategies often outperform unimodal and shallow approaches. Additionally, the proposed subcategories of fusion strategies show different advantages and drawbacks. The review of current methods has shown that, especially for intermediate fusion strategies, joint representation learning is the preferred approach as it effectively models the complex interactions of different levels of biological organization. Finally, we note that gradual fusion, based on prior biological knowledge or on search strategies, is a promising future research path. Similarly, utilizing transfer learning might overcome sample size limitations of multimodal data sets. As these data sets become increasingly available, multimodal DL approaches present the opportunity to train holistic models that can learn the complex regulatory dynamics behind health and disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.