Genomic data sharing is crucial for advancing treatment, especially for rare diseases and optimizing patient care, yet it faces significant hurdles due to privacy and security concerns. Healthcare institutions are slow to integrate advanced genomic data analysis techniques, primarily because of the sensitive nature of healthcare data and the need to balance privacy with utility. Experts like Dr. Alon Kaufman and Dr. Kinneret Livnat-Savitzky emphasize the importance of privacy-preserving technologies, machine learning, and artificial intelligence in facilitating secure data sharing that maintains trust and regulatory compliance. By linking genomic and clinical data, researchers can uncover risk factors and improve patient outcomes, although privacy issues often limit data accessibility. Solutions such as data decentralization and encryption are proposed, with an emphasis on creating a privacy-preserving framework that supports innovation in genomic research. Additionally, the integration of real-world data from various sources can provide a holistic view of patient health, allowing for more tailored and effective treatments. Despite regulatory challenges, there is growing recognition of the need for collaborative approaches in healthcare to fully realize the benefits of genomic data sharing.