Recent advances in computational methods have greatly improved the understanding of scientists, researchers, and medical practitioners about the physiology and functioning of human systems, health, and medicine. Specifically, artificial intelligence (AI) based computational methods like deep learning have been outstanding in many medical applications from disease detection to mortality prediction to optimal hospital resource management.

Because advanced computational methods-based medical diagnostic systems may offer cheap and reliable solutions for many problems in the medical domain, AI researchers have recently focused on building diagnostic models for the early detection of diseases, mortality prediction, and hospital resource management. Such computational models leverage mobile health (mHealth) data. The data is collected from wearable sensors and smartphones to discover novel ways of detecting and managing chronic diseases and mental health conditions. However, the main limitation of these methods is a low generalization rate during independent testing.

The aim of this Special Issue is to invite researchers and medical practitioners to propose novel ideas or applications of state-of-the-art computational methods for healthcare and medical applications. Original research and review articles are welcomed.

Potential topics include but are not limited to the following:

• Advances in computational methods for medical applications
• Machine learning methods for efficient diagnostic systems
• Data mining methods for disease detection systems
• Deep learning-based computational methods for disease detection systems
• Machine learning and deep learning-based mortality prediction systems
• Machine learning-based telediagnosis and telemonitoring of diseases
• Machine learning methods for drug discovery
• Novel computational methods for hospital resource management
• Heuristic optimization methods for optimal medical applications