Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects

TL;DR

This survey reviews the current state and future prospects of multimodal machine learning in biomedical image analysis and clinical decision support, highlighting challenges, innovations, and the need for principled evaluation.

Contribution

It provides a comprehensive overview of multimodal ML applications in biomedicine, emphasizing challenges, recent innovations, and future directions for clinical integration.

Findings

Multimodal ML enhances medical image analysis and clinical predictions.

Challenges include data biases and limited big data availability.

The paper advocates for principled assessment and collaborative development.

Abstract

Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML, focusing on its profound impact on medical image analysis and clinical decision support systems. Emphasizing challenges and innovations in addressing multimodal representation, fusion, translation, alignment, and co-learning, the paper explores the transformative potential of multimodal models for clinical predictions. It also highlights the need for principled assessments and practical implementation of such models, bringing attention to the dynamics between decision support systems and healthcare providers and personnel. Despite advancements, challenges such as data biases and the scarcity of "big data" in many biomedical domains persist. We conclude with a discussion on principled innovation and collaborative efforts to further the mission of seamless integration of multimodal ML models into biomedical practice.