# Multimodal artificial intelligence in medicine: a task-oriented framework for clinical translation

## TL;DR

This paper reviews how combining different data types with AI improves medical diagnosis and treatment planning, helping doctors make better decisions.

## Contribution

The paper introduces a task-oriented framework for multimodal AI in clinical settings, emphasizing data fusion and interpretability.

## Key findings

- Multimodal AI systems outperform unimodal models in diagnostic accuracy and prognostic prediction.
- Robust data fusion strategies and model interpretability are crucial for clinical deployment.
- Multimodal AI can enhance personalized medicine and patient outcomes.

## Abstract

Multimodal artificial intelligence (AI) technologies are transforming medical practices by integrating diverse data sources to enable more accurate diagnosis, disease prediction, and treatment planning. In this review, we explore state-of-the-art multimodal AI systems, focusing on their applications in clinical settings, including radiology, pathology, and clinical imaging, as well as non-image data, such as electronic health records (EHRs) and multi-omics data. We highlight how combining multiple modalities improves diagnostic accuracy and prognostic prediction compared to unimodal models. The study emphasizes the importance of robust data fusion strategies and model interpretability for real-world clinical deployment. By addressing key challenges, such as data heterogeneity and uncertainty quantification, this research offers a new paradigm for intelligent healthcare. The findings suggest that the continued advancement of multimodal AI will significantly enhance clinical decision-making, paving the way for personalized medicine and improved patient outcomes.