A Review on AI-Driven Multi-Modal Data Integration in Personalized Medicine: Advancements in Diagnosis, Prognosis, and Treatment Optimization

The merging of various data types constitutes an innovative medical strategy which enhances physicians' abilities to detect and predict diseases while creating better therapy protocols. Artificial intelligence (AI) along with deep learning models improve precision healthcare by analyzing various data sources which encompass genomic sequences and electronic health records (EHRs) with medical imaging information and information gathered from wearable sensors. This review discusses essential findings from multi-modal data studies about diagnostic tools developed with AI methods as well as predictive models that forecast therapy results and novel biomarker identification techniques. This paper elaborates on methodological techniques and points out dominant patterns before discussing existing knowledge deficits scientists must resolve. A discussion about data heterogeneity, interpretability issues and privacy challenges emerges during the exploration of future methods to improve AI-driven multi-modal integration in clinical practice.