# Multi-modal AI in precision medicine: integrating genomics, imaging, and EHR data for clinical insights

## TL;DR

This paper explores how combining genomics, imaging, and health records with AI can improve personalized healthcare and treatment strategies.

## Contribution

The paper reviews how multi-modal AI integration advances precision medicine through enhanced data analysis and clinical decision-making.

## Key findings

- Multi-modal AI integration improves diagnostic precision and enables personalized therapeutic interventions.
- AI algorithms help uncover complex associations across diverse data types like genomics and EHRs.
- Digital health tools powered by AI allow continuous treatment refinement through real-time monitoring.

## Abstract

Precision healthcare is increasingly oriented toward the development of therapeutic strategies that are as individualized as the patients receiving them. Central to this paradigm shift is artificial intelligence (AI)-enabled multi-modal data integration, which consolidates heterogeneous data streams—including genomic, transcriptomic, proteomic, imaging, environmental, and electronic health record (EHR) data into a unified analytical framework. This integrative approach enhances early disease detection, facilitates the discovery of clinically actionable biomarkers, and accelerates rational drug development, with particularly significant implications for oncology, neurology, and cardiovascular medicine. Advanced machine learning (ML) and deep learning (DL) algorithms are capable of extracting complex, non-linear associations across data modalities, thereby improving diagnostic precision, enabling robust risk stratification, and informing patient-specific therapeutic interventions. Furthermore, AI-driven applications in digital health, such as wearable biosensors and real-time physiological monitoring, allow for continuous, dynamic refinement of treatment plans. This review examines the transformative potential of multi-modal AI in precision medicine, with emphasis on its role in multi-omics data integration, predictive modeling, and clinical decision support. In parallel, it critically evaluates prevailing challenges, including data interoperability, algorithmic bias, and ethical considerations surrounding patient privacy. The synergistic convergence of AI and multi-modal data represents not merely a technological innovation but a fundamental redefinition of individualized healthcare delivery.