Integrated multimodal artificial intelligence framework for healthcare applications

The integration of multimodal artificial intelligence in healthcare has opened up a new era in diagnostic accuracy, personalized treatment, and efficient clinical workflows. Using myriad data types such as imaging, clinical notes, genomic data, or signals from sensors, such multimodal AI systems create a holistic and context-aware picture of patient health. This paper reviews recent advancements of multimodal AI applications in diagnostics, therapeutics, and patient monitoring, focusing on the synergy between modalities such as vision and language and structured data. It also addresses the technical challenges such as data harmonization, interpretability, and ethics, while providing solutions in building more robust models and better integrating AI into clinical practice. Finally, it discusses prospective research directions and policy guidelines required for globally conscious, responsible adoption of multimodal AI in healthcare.