Artificial Intelligence in Predictive Analytics of Patient Outcomes and Disease Management

Abstract

Artificial Intelligence (AI) has revolutionised predictive analytics in healthcare, offering innovative approaches for patient outcome prediction and disease management. This review explores the role of AI-driven predictive models in early disease detection, prognosis estimation, and personalised treatment strategies. This review also discusses recent advancements, limitations, and future prospects of AI-powered predictive analytics in healthcare, emphasising its transformative potential in improving patient care and disease prevention. Machine learning (ML) algorithms, deep learning (DL) networks, and natural language processing (NLP) have significantly enhanced predictive capabilities by analysing vast datasets, including electronic health records (EHRs), genetic information, and real-time patient monitoring data. AI applications in disease management facilitate early intervention, optimise resource allocation, and improve clinical decision-making. However, challenges such as data privacy, model interpretability, and ethical considerations remain key concerns. Ensuring transparency and explainability in AI models is crucial to gaining clinician and patient trust while mitigating risks associated with biased or erroneous predictions.