Although telehealth technology has been present for years, appearance of the Covid-19 pandemic dramatically has accelerated its growth. Telehealth connects patients to vital health care services through videoconferencing, remote monitoring, electronic consults, and wireless communications. By increasing access to physicians and specialists, telehealth helps ensure patients receive the right care at the right place and at the right time. Investment currently is strong for numerous types of telehealth systems, many including AI components, and leading enterprises in this work are now recognizing the important need for participatory design. Advances in artificial intelligence tools and methods provide better insights, reduce waste and wait time, and increase speed, service efficiency, level of accuracy, and productivity in telehealth and medicine. Moreover, new initiatives such as precision health and medicine emphasize the importance of focusing on individuals’ risk factors for disease prevention, early diagnosis, and intervention. Most of the time complex automatic decision support systems, and Black-Box machine learning-based artificial intelligence models lack proper explanation. Explainable Artificial Intelligence (XAI) addresses some of the restrictions of a Black-box AI system by explaining and interpreting their diagnosis, predictions, and recommended actions to stakeholders. It aims to create more understandable, interpretable, and reliable models, by improving the quality of predictions.

Multimedia computing with Explainable Artificial Intelligence for telehealth has the potential to revolutionize many aspects of our society; however, many technical challenges need to be addressed before this potential can be realized. Some of these challenges include: 1. How the potential of multimedia computing with Explainable Artificial Intelligence can provide exciting and meaningful insights to researchers for new opportunities in this field? 2. How these technologies can assist with the right patient care at the right time and in the right place? 3. How telehealth multimedia computing with Explainable Artificial Intelligence can facilitate healthcare data representation, storage, analysis, and integration for effective smart healthcare solutions?

This Special Issue is intended to report high-quality original research and review articles on recent advances toward multimedia computing with Explainable Artificial Intelligence for telehealth, more specifically on the state-of-the-art approaches, methodologies, and systems for the design, development, deployment and innovative use of those convergence technologies for providing insights into telehealth service demands.

Potential topics include but are not limited to the following:

• Open research challenges and directions for Explainable Artificial Intelligence for telehealth
• New theories, models, and benchmarks for telehealth multimedia computing with XAI
• Explainable deep learning architectures and algorithms for large-scale healthcare multimedia data
• Interpretability in reinforcement learning for telehealth multimedia
• Verifying, diagnosing and debugging machine learning systems for telehealth multimedia
• Fairness, accountability, and transparency in multimedia XAI
• Deep Learning-based networked applications, techniques and testbeds of interactive multimedia for telehealth