AI-Driven Predictive Analytics for Patient Outcomes

The integration of artificial intelligence (AI) in healthcare has introduced transformative possibilities, particularly through predictive analytics aimed at enhancing patient outcomes. This paper explores the application of AI-driven predictive analytics in forecasting patient outcomes, focusing on methodologies, benefits, challenges, and future prospects. By leveraging large volumes of medical data, AI algorithms can identify patterns and make accurate predictions, improving patient care and optimizing healthcare resources. This research delves into various AI techniques, including machine learning and deep learning, and their applications in predicting disease progression, hospital readmissions, and treatment responses. It also examines the ethical and regulatory considerations, addressing concerns related to data privacy and algorithmic bias. The paper concludes with a discussion on the future directions of AI in healthcare, emphasizing the need for robust validation and seamless integration into clinical workflows. The findings highlight the transformative potential of AI-driven predictive analytics in enhancing personalized medicine and ultimately contributing to better patient outcomes and healthcare efficiency.

کلیدواژه ها:

AI ، Predictive Analytics ، Patient Outcomes ، Healthcare ، Machine Learning ، Deep Learning ، Personalized Medicine ، Data Privacy ، Algorithmic Bias