AI-Driven Insights In Disease Prediction And Prevention: The Role Of Cloud Computing In Scalable Healthcare Delivery

Abstract

Artificial Intelligence (AI) has gained considerable importance in numerous domains, from daily life activities to large-scale scientific applications. Recent advances in computing systems, algorithms, and enhanced applications for big data processing and data mining have prompted a grand shift towards AI-focused operations. AI has the potential to improve the lives of patients, physicians, and hospital managers by doing activities usually performed by human beings in a fraction of the time taken and a fraction of the cost. For instance, AI aids physicians in advanced decision-making by evaluating gargantuan amounts of healthcare data to improve health outcomes and eventually save the life of the patient. Similarly, this data aids in enhancing and accelerating decision-making while diagnosing and treating patients’ illnesses using artificial intelligence-based techniques. AI helps physicians detect diseases by utilizing complicated algorithms, hundreds (sometimes thousands) of biomarkers, imaging findings from millions of patients, aggregated published clinical studies, and thousands of physicians’ notes to improve the accuracy of diagnosis.
Falling prey to diseases is a serious concern faced by all individuals, and events or behavior patterns preceding the outbreak of such diseases are considered the most critical points to research on. The fateful arrival of a disease could be predicted on such behavior patterns, and appropriate preventive measures could be good medications, detection/diagnosis recommendations, or others. Although many attempts have been made regarding the early detection of diseases from health records or social media, there are still many unexplored modes or domains for pattern investigation and development of prediction models on them. Machine learning is an efficient approach for analyzing enormous volumes of historical data and predicting behavior patterns of individuals/businesses in innumerable domains. To date, many instances of machine-learning methods have been used in the prediction of diseases, improvement of preventive measures, and handling of outbreaks in various healthcare domains.