Revolutionizing Healthcare Management with Artificial Intelligence: Addressing Challenges in Implementation and Scalability

Abstract

Artificial Intelligence (AI) now offers enhanced diagnostic precision, improved treatment planning and operational efficiency to the healthcare management arena. In this study, four models including Decision Tree, Support Vector Machine (SVM), Random Forest, and Deep Neural Network (DNN) are used to study deployment, scalability of AI algorithms in healthcare settings. Synthetic healthcare data was used to perform an in depth analysis on all the algorithms in terms of comparison of Accuracy, Precision, Recall and Run time. The results showed that DNN was the best by having 95.2% of accuracy, followed by Random Forest with 91.7%, SVM with 89.3%, and then Decision Tree with 86.5%. For complex medical data, the recall and F1-score results with DNN were the highest, and therefore it was stable. While precise, DNN required more computational power in order for its approximation's error to become negligible. The research also extends these findings to existing literature and shows improvements in scalability and performance in this process. The results motivate the strategic adoption of AI models, such as DNN, in real healthcare systems and better explainability, ethical regulation, and infrastructure readiness. The study, therefore, provides a basis for future research to generate low cost and patient specified AI models in healthcare administration.