Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey

TL;DR

This survey evaluates whether multimodal learning has achieved universal intelligence in healthcare, concluding that current technologies have not yet reached this goal and identifying future research directions.

Contribution

It provides a comprehensive analysis of medical multimodal learning progress, challenges, and future directions from multiple perspectives.

Findings

Current technologies have not achieved universal intelligence in healthcare.

Extensive datasets and methods exist but face limitations.

Ten future research directions are proposed.

Abstract

The rapid development of artificial intelligence has constantly reshaped the field of intelligent healthcare and medicine. As a vital technology, multimodal learning has increasingly garnered interest due to data complementarity, comprehensive modeling form, and great application potential. Currently, numerous researchers are dedicating their attention to this field, conducting extensive studies and constructing abundant intelligent systems. Naturally, an open question arises that has multimodal learning delivered universal intelligence in healthcare? To answer the question, we adopt three unique viewpoints for a holistic analysis. Firstly, we conduct a comprehensive survey of the current progress of medical multimodal learning from the perspectives of datasets, task-oriented methods, and universal foundation models. Based on them, we further discuss the proposed question from five issues to explore the real impacts of advanced techniques in healthcare, from data and technologies to performance and ethics. The answer is that current technologies have NOT achieved universal intelligence and there remains a significant journey to undertake. Finally, in light of the above reviews and discussions, we point out ten potential directions for exploration towards the goal of universal intelligence in healthcare.