ReviewGenomics, proteomics & bioinformatics2025
Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.
Review in Genomics, proteomics & bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Artificial intelligence-guided nanozyme engineering for chronic wound healing: from rational design to precision therapeutics.Bioactive materials · 2027Review
- Temporomandibular Disorders Diagnosis: Current Challenges and the Promising Role of Artificial Intelligence.European journal of dentistry · 2026Article
- [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
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- Review
- Machine learning enhanced optical spectroscopy for breast cancer diagnosis: A review.Lasers in medical science · 2026Review
- Artificial intelligence revolutionizing CNS drug discovery and development.Drug discovery today · 2026Review
- Benchmarking Multimodal Deep Fusion Strategies for Heterogeneous Neuroimaging and Cognitive Data Using a Controlled Sex Classification Task.Brain sciences · 2026Article
- Grounded report generation for enhancing ophthalmic ultrasound interpretation using Vision-Language Segmentation models.NPJ digital medicine · 2026Article
- Multimodal data integration in orthopedic regenerative medicine: bridging imaging, omics, and clinical data.Frontiers in cell and developmental biology · 2026Review
- Radiomics and deep learning in upper tract urothelial carcinoma: advancing preoperative risk stratification and clinical decision-making.Frontiers in oncology · 2026Review
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Training the next-generation of biomedical scientists through artificial intelligence-driven education and research in pharmacology and pharmaceutical sciences.Experimental biology and medicine (Maywood, N.J.) · 2026Review
- Artificial Intelligence in Organoid-Based Disease Modeling: A New Frontier in Precision Medicine.Biomimetics (Basel, Switzerland) · 2025Review
- Cell-free DNA in sepsis: from molecular insights to clinical management.Military Medical Research · 2025Review
- Review
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- Joint similarity nonnegative matrix factorization model for identification of recurrence-related association patterns in tumor.Briefings in bioinformatics · 2025Article
- Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems.Journal of translational medicine · 2025Review
- Novel cancer subtyping method guided by tumor-normal sample in latent space of transcriptomic variational autoencoder.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of biological and medical examination methods has vastly expanded personal biomedical information, including molecular, cellular, image, and electronic health record datasets. Integrating this wealth of information enables precise disease diagnosis, biomarker identification, and treatment design in clinical settings. Artificial intelligence (AI) techniques, particularly deep learning models, have been extensively employed in biomedical applications, demonstrating increased precision, efficiency, and generalization. The success of the large language and vision models further significantly extends their biomedical applications. However, challenges remain in learning these multimodal biomedical datasets, such as data privacy, fusion, and model interpretation. In this review, we provide a comprehensive overview of various biomedical data modalities, multimodal representation learning methods, and the applications of AI in biomedical data integrative analysis. Additionally, we discuss the challenges in applying these deep learning methods and how to better integrate them into biomedical scenarios. We then propose future directions for adapting deep learning methods with model pretraining and knowledge integration to advance biomedical research and benefit their clinical applications.
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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.