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Challenges and proposed solutions in modeling multimodal medical data: A systematic review

arXiv 2505.06945 ↗First posted 11 May 2025cs.LG stat.MLcited by 0

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Abstract (arXiv)

Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 130 references that are in the map. First posted on arXiv 11 May 2025.

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References · 26

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  2. 2020Knowledge Distillation: A Surveycited by 1,603
  3. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
  4. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 909
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  7. 2021Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysiscited by 180
  8. 2023Multi-modal Learning with Missing Modality via Shared-Specific Feature Modellingcited by 163
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  11. 2021A Variational Information Bottleneck Approach to Multi-Omics Data Integrationcited by 40
  12. 2024Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generationcited by 31
14 more
  1. 2023A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdomcited by 25
  2. 2022Multimodal Attention-based Deep Learning for Alzheimer's Disease Diagnosiscited by 24
  3. 2017Random Forest Missing Data Algorithmscited by 23
  4. 2019Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Datacited by 23
  5. 2022Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Datacited by 18
  6. 2019Multimodal Subspace Support Vector Data Descriptioncited by 15
  7. 2019Polynomial Matrix Completion for Missing Data Imputation and Transductive Learningcited by 15
  8. 2022TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosiscited by 15
  9. 2021MRI-based Alzheimer's disease prediction via distilling the knowledge in multi-modal datacited by 9
  10. 2013Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classificationcited by 7
  11. 2022CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patientscited by 6
  12. 2024HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Modelscited by 6
  13. 2018Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completioncited by 5
  14. 2024Multimodal Approaches to Fair Image Classification: An Ethical Perspectivecited by 1

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