Artificial Intelligence in Healthcare Data Analytics: A Comprehensive Review of Methods, Applications, and Challenges

Abstract

Artificial intelligence (AI) is quickly revolutionizing healthcare data analytics, as it allows the insights to be extracted in meanings in complex and big data. This review focuses on some of the most prominent AI methods, such as machine learning, deep learning, natural language processing, and computer vision, and how they can be used in disease diagnosis, predictive modeling, personalized medicine, medical imaging, clinical decision support, and healthcare operations. The paper also addresses the key issues of data quality, bias, privacy, interpretability, and workflow integration, pointing out such new directions as explainable AI, privacy-preserving analytics, multimodal data integration, and the Internet of Medical Things. The examination highlights the opportunities presented by AI to enhance the sphere of clinical decision-making, operational effectiveness, and patient-centered care, and it is crucial that ethical, transparent, and scalable implementation will be emphasized as critical to the future healthcare systems.