This chapter aims to present essential insights into the use of computer technology and the construction of mathematical models for the development of healthcare engineering with focus on early disease diagnosis, medication control, and medical image processing. Recent developments and possible future directions of biomedical models are discussed based on differential equations, statistics, machine learning, and network science. Such models are important to represent the dispersion of medication, refine cancer therapies, and enhance the quality of images used in diagnosing patients. Drug-protein interactions, a method that belongs to network science, help to predict targets of the drugs, their metabolisms, and subsequent doses and toxicity levels. However, the chapter also includes different types of problems and issues, including the issue of data quality and volume, the privacy and protection of patients data, computational complexity, and the need for interpretable and explainable models. Negotiating such hurdles is crucial for the enhancement of integrating mathematical models into clinical decisions. It stresses the need to pay close attention to the quality of data, the number of samples needed, correct and appropriate model selection, and continuous learning to minimize the risk of having ineffective and unreliable models when implemented in real-life situations. © 2025 Elsevier B.V., All rights reserved.