Healthcare requires a substantial amount of data from patients. Artificial intelligence (AI) is a technology that is based on a mathematical model or algorithm that makes decisions based on existing data. It simulates a human's decision-making skills. During the training phase, it develops a self-correction method based on unsupervised learning and reasoning. AI has only developed recently from many years of study and development. It is capable of identifying and rectifying the problems of uncertainty. It does so by using unsupervised data analytics and deep learning methods. To make AI a more convenient tool in our daily life, we must take unsupervised learning principles to improve the efficiency and accuracy rate of health-based application systems.

Meanwhile, intelligent healthcare applications are becoming extremely relevant as the automation of health monitoring and disease diagnosis becomes increasingly vital. They are commonly used for health index monitoring based on unsupervised learning, drug discovery, medical imaging diagnosis, Alzheimer's disease, genomics, and more. Unfortunately, some logistics and supply issues can cause the process of the healthcare system to fail. Unsupervised learning can be used to overcome limitations and improve the efficiency of healthcare applications. Research connecting healthcare applications, AI with deep learning algorithms, unsupervised learning methods can improve the healthcare system process. Healthcare is a priority and researchers are continually searching for better solutions.

The aim of this Special Issue is to bring together original research and review articles that highlight the challenges and trends of unsupervised learning and deep learning methods in healthcare.

Potential topics include but are not limited to the following:

• Intelligent health monitoring and management based on big data
• Intelligent disease diagnosis based on unsupervised learning
• Medical robots and imaging diagnosis based on computer vision technology
• Drug research and development based on unsupervised learning
• Natural language processing and speech technology in electronic medical records
• Intelligent unsupervised learning nursing system
• Epidemic forecasting based on intelligent algorithm and mathematical modelling