Artificial intelligence (AI) is a major research area with many real-world applications such as business, engineering, and healthcare. In particular, the introduction of different and new techniques in machine learning and data mining provide efficient tools to assisted systems in healthcare such as medical diagnostics and patient monitoring. The techniques are widely used as a screening tool or as an aid for diagnosis so that fast and informed decisions can be made, especially those with big data sets from multiple sources. However, the healthcare systems need to be upgraded with new artificial intelligence techniques to provide more intelligent and professional services.

Despite the great advancements and breakthroughs so far, the black-box nature of AI hinders its applications in the healthcare industry. This situation is even worse in complex data analytics. It is imperative to develop explainable AI models to provide safe, reliable, and efficient solutions integrated into healthcare applications.

This Special Issue intends to gather the latest high-quality research studies of explainable AI techniques, along with other machine learning algorithms for assisted healthcare systems. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Explainable AI models for healthcare systems
• Explainable AI for daily living activities and health risks
• Neural networks and fuzzy logic-based interpretable healthcare systems
• Machine learning algorithms for medical imaging and pattern recognition
• Bioinspired algorithms for healthcare systems
• Novel AI theory and algorithms
• Intelligent wearable and assistive robotic devices for healthcare
• AI-based health recommender systems
• AI in healthcare informatics
• Big data analytics for healthcare management