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

TL;DR

This systematic review analyzes the challenges in multimodal data modeling in clinical research, highlighting recent methodological advances like transfer learning and neural architecture search to address issues such as data heterogeneity and interpretability.

Contribution

It synthesizes findings from 69 studies, identifying key obstacles and recent solutions, providing a comprehensive overview and practical insights for future multimodal modeling research in medicine.

Findings

Identified common challenges like missing data and modality imbalance.

Highlighted recent advances such as transfer learning and attention mechanisms.

Provided a roadmap for future research directions in multimodal medical data modeling.

Abstract

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.