Published Paper Details:

Predictive analytics powered by artificial intelligence (AI) is transforming preventive healthcare through early disease detection, risk assessment, and timely intervention. Traditionally, healthcare systems have responded reactively to disease evolution and treatment, with symptoms being observed at the last moment; thus, most of the time, it has increased healthcare expenses for a poor outcome for the patient. Conversely, predictive models powered by AI capitalize on large datasets such as electronic health records (EHRs), medical imaging, genetic profiles, and real-time data from wearable devices to reasonably predict any possible health risk prior to the clinical manifestation of the symptoms (Jiang et al., 2017). This proactive strategy is especially beneficial for dealing with chronic diseases, including diabetes, cardiovascular events, and neurodegenerative disorders, whereby early detection and intervention enormously decrease mortality rates and improve the quality of life for patients (Topol, 2019). Machine learning (ML) and deep learning techniques are the mainstay of predictive analytics, recognizing patterns in complex medical data that could be inconspicuous to human clinicians. For example, convolutional neural networks (CNN) have found enormous success in analyzing medical images for early-stage cancer detections and have outperformed the traditional methods with respect to sensitivity and specificity (Ardila et al., 2019). Along similar lines, in cardiology, neural networks have been used to analyze RNNs by predicting high-risk sudden cardiac arrest patients on ECG readings whereby preventive measures can easily be taken within the appropriate time (Attia et al., 2019). AI-driven prediction tools have an extended application in hospitals to predict patient deterioration, helping in better resource allocation and a reduction in unplanned hospital readmissions (Rajkomar et al., 2018). Predictive healthcare provided by AI enjoys morbidity and mortality improvements at levels higher than individual patient care. In other words, massive AI models coupled with big data land in epidemiological surveillance, as illustrated during the COVID-19 pandemic. So AI algorithms were used to analyze vast amounts of mobility data, social media posts, and clinical reports just for the purpose of tracking infection patterns, predicting outbreak hotspots, and assisting in planning vaccine distribution (Rahmani et al., 2021). AI predictive analytics is now beginning to influence precision medicine, in which treatments are customized to a patient's unique genetic and physiological characteristics rather than employing a one-size-fits-all approach (Shen et al., 2020). Despite AI's great promise, there are barriers in most areas to universal acceptance of predictive analytics in aid of AI. The ethical and regulatory questions that arise concerning privacy and algorithmic bias also address issues related transparency in AI's decision-making (Morley et al., 2020). Furthermore, a heavy investment is needed to put AI into existing healthcare workflows, which would cover healthcare infrastructure, training of clinicians to use these tools, and ongoing collaboration between data scientists and medical professionals (Esteva et al., 2019). Getting around these problems is going to be fundamental for AI to fulfill its promise in predictive health care. The paper looks at AI predictive analytics in preventive health care in a broad sense, addressing its methods, uses, benefits, and restrictions. We hope, through case studies and recent advancements, to show the role of AI in actually transforming global health care from the current reactive model toward a preventive framework with better patient health outcomes and lesser burden on health care systems around the world.