Integrating Big Data and Cognitive Visual Environment for Precision Medicine: Opportunities and Challenges

Precision medicine has shown promise in integrating Big Data analytics with Cognitive Visual Environment approaches to advance individualized patient treatment. The main conclusions from the study of various data sources are presented in this article, along with an exploration of the potential and difficulties connected with this integration. A thorough insight of patient health profiles was attained by combining medical imaging, genetic data, and electronic health records. predicting model creation was made easier by the use of Big Data analytics techniques, and the addition of Cognitive Visual Environment approaches improved the predicting powers by including visual data from medical imaging data. The personalized treatment recommendations generated through this integration offer the potential for improved treatment outcomes. Moreover, the utilization of Cognitive Visual Environment techniques enabled enhanced visual analytics and decision support for healthcare professionals. Despite the promising results, challenges such as data integration complexities and ethical considerations need to be addressed. Future research directions involve incorporating real-time data, expanding data sources, and validating the effectiveness of the integrated approach through clinical trials. This integration of Big Data analytics and Cognitive Visual Environment represents a transformative opportunity in precision medicine, enabling a more precise and individualized approach to patient care.