Multimodal Machine Learning Approaches in Predictive Healthcare Analytics: A Comprehensive Survey

Abstract

This survey explores the application of multimodal machine learning techniques in predictive healthcare analytics. By integrating various data modalities, such as medical imaging, clinical text, time-series signals, and structured tabular data, these approaches aim to emulate clinical reasoning and enhance diagnostic and prognostic accuracy. Across studies, multimodal ML consistently outperformed unimodal baselines, with intermediate fusion employed in 60% of cases and achieving average AUC improvements of 5–12% over single-modality models. Oncology and neurology emerged as the leading domains, where combining imaging with genomic and cognitive data significantly improved cancer survival and Alzheimer’s detection. Despite progress, key challenges persist, including modality misalignment (23%), missing data (18%), and limited external validation (12%). Recent trends highlight transformer-based cross-modal attention, self-supervised learning for data-scarce settings, and hybrid fusion architectures in critical care. While multimodal ML offers clear clinical advantages, regulatory constraints and interoperability gaps continue to hinder deployment. This survey contributes to the existing literature by providing a comprehensive synthesis of multimodal ML applications across healthcare domains. It documents comparative fusion strategies, modelling approaches, and empirical performance outcomes. The paper’s primary contribution lies in identifying intermediate fusion as the most effective integration strategy and revealing systematic gaps in external validation and model transparency that must be addressed for clinically trustworthy multimodal systems.

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