Advancing healthcare transformation: AI-driven precision medicine and scalable innovations through data analytics

Artificial intelligence and data fusion technologies are being used to incorporate the technology into healthcare systems worldwide.  This work focuses on the idea and investigates how the AI-based Data Fusion Centre affects precision medicine, organizational and patient-centric models. In understanding how practice, diagnostics, and general efficiency of the healthcare system may benefit from AI, this article provides a great example. In this way, the approach is extended comprehensively by providing an analysis of techniques and illustrations. A unique case surveillance at Cleveland Clinic made it possible for the authors to record the influence of data fusion centers. Data fusion integrates as needed multiple data originating from various data fusion centers and provides a coherent and inclusive health status for a given patient. Some examples are genetics databases, electronic health records databases, wearable sensors in real-time databases. Contemporary diagnostic tools’ feasibility and efficacy are explained through methodologies based on machine learning and deep learning. These studies have helped in early diagnosis of the illness signals and cost parameters minimization. Analyzing this article one can observe that the growing ethical considerations to be met are to allow intelligent machines to work in full efficiency. The problem area that has come up in relation to GCP is data privacy, which is viewed as a major concern, second to algorithmic bias and integration. The results of the study show that it is expected that Data Fusion Center offers pro and post progressively effective and fair president, especially on the health of the clients.