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Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.

Meicheng Yang¹, Nan Shi², Hui Chen²

¹School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.

Health Data Science

|April 20, 2026

Summary

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This summary is machine-generated.

Multimodal artificial intelligence (AI) integrating diverse patient data shows promise for critical care. This review highlights AI

Area of Science:

Critical care medicine
Artificial intelligence
Data science

Background:

Intensive care units (ICUs) generate vast, complex patient data.
Multimodal artificial intelligence (AI) offers potential for precision medicine in critical care.
Comprehensive reviews on multimodal AI in ICUs are limited.

Purpose of the Study:

To systematically review the progress, methods, and challenges of multimodal AI in critical care.
To provide a roadmap for developing and implementing multimodal AI in ICUs.

Main Methods:

Scoping review following PRISMA-ScR guidelines.
Systematic literature search across major databases (PubMed, EMBASE, Scopus, Web of Science, IEEE Xplore) from 2010-2025.
Inclusion of studies integrating at least two data modalities using AI for critical care outcomes.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Main Results:

86 studies included, 85% published in the last 5 years.
Common modalities: structured data, text, imaging, waveforms, videos.
Intermediate fusion was the primary integration strategy.
Multimodal AI achieved a 4.4% relative improvement in AUC for diagnostic/prognostic tasks compared to unimodal models.

Conclusions:

Multimodal AI demonstrates superior performance in critical care tasks.
This review offers a strategic framework for multimodal AI design, evaluation, and implementation in the ICU.
The ultimate goal is to enhance patient outcomes through advanced AI integration.