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The integration of Artificial Intelligence (AI) in healthcare has ushered in a new era of predictive analytics for disease diagnosis and treatment. AI-driven healthcare predictive analytics leverages vast amounts of medical data, employing advanced machine learning and deep learning techniques to identify patterns and predict health outcomes. This approach enhances diagnostic accuracy, enables early detection of diseases, and personalizes treatment plans, thereby improving patient outcomes and optimizing healthcare resources. AI models can analyze diverse data sources, including electronic health records (EHRs), medical imaging, and genetic information, to provide comprehensive insights into patient health. Despite its potential, the implementation of AI in healthcare faces challenges such as data privacy concerns, the need for large, high-quality datasets, and the integration of AI systems into existing clinical workflows. This abstract reviews the current state of AI-driven healthcare predictive analytics, highlights key advancements, and discusses the challenges and future directions for the effective use of AI in disease diagnosis and treatment. By addressing these challenges, AI has the potential to revolutionize healthcare, making it morepredictive, precise, and personalized.
Babu et al. (Tue,) studied this question.