AI, ML, and Data Analytics at the Core of Predictive Healthcare and Smart Medical Supply Chains: Systematic Perspectives

Abstract

The fast development of the Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics is changing the sphere of healthcare with the predictive models of healthcare and intelligent medical supply chains. The predictive healthcare uses evidence-based information to understand the risk factors of patients, allow early identification of diseases, and allow tailored treatment plans, whereas smart supply chains can improve access to the required medical materials and enhance their distribution. Combining these domains into one active, flexible ecosystem makes clinical forecasting and logistics planning to be in harmony, which leads to improved patient outcomes and reduced operational costs. Resilience and transparency of the systems are also improved by emerging technologies such as digital twins, IoT, automation, and explainable AI. Although data quality, interoperability, ethical concerns, and regulatory standards are some of the challenges of the convergence of AI, ML and data analytics, the potential impact of the convergence of AI, ML and data analytics on healthcare delivery is immense. This review reviews the existing trends, applications, constraints, and directions, and how computational intelligence can streamline both treatments and management of medical resources to provide a sustainable, efficient and patient-centred healthcare ecosystem.