Facilitating Precision Medicine with Computer Vision and AI-Driven Multi-Modal Data Analysis: Combining Imaging, Genomics, and Electronic Health Records for Holistic Patient Profiling

Abstract

Precision medicine aims to tailor medical treatment and prevention strategies to individual patients based on their unique characteristics, including genetic makeup, lifestyle factors, and medical history. The integration of computer vision and artificial intelligence (AI) techniques in the analysis of multi-modal medical data offers a powerful approach to enable precision medicine by providing comprehensive patient profiles. This research article explores the potential of leveraging computer vision and AI algorithms to integrate and analyze diverse medical data sources, such as medical imaging, genomics, and electronic health records (EHRs). By examining the current state of research, case studies, and future prospects, we aim to highlight the transformative potential of these technologies in enabling personalized medicine, improving diagnostic accuracy, and optimizing treatment strategies. The article also discusses the challenges and considerations associated with the implementation of AI-driven multi-modal data analysis in clinical practice, including data interoperability, privacy concerns, and the need for interdisciplinary collaboration.