With the rising costs of drugs, medical devices, and diagnostic development, in addition to pressures to lower prescription and procedural costs as well as a demand for more personalized medicines, the healthcare industry is facing considerable pressure. Medical informatics focuses on information technology that positively impacts the patient-physician relationship through effective collection, safeguarding, and understanding of health data. This technology is playing an increasingly important role in the context of medical big data.

The goal of computational healthcare and bioinformatics is to transform how people are kept safe and healthy, especially with the increasing demand for solutions to lower healthcare costs in the coming years. Advanced methods and technologies for medical informatics can help monitor, inform, and notify caregivers as well as healthcare providers with actual data for early intervention. However, analyzing and processing medical big data is a great challenge due to its complex and unstructured characteristics such as volume, variety, velocity, value, sequence, strong-relevance, accuracy, and closed-loop. Moreover, medical big data processing technology is experiencing revolutionary transformations in each stage including data collecting, cleaning, organizing, interpreting, analytics, utilizing, and visualization. Many of the above problems can be tackled with advanced methods such as advanced artificial intelligence solutions. Recently, advanced artificial intelligence methods such as deep learning, deep reinforcement learning, few-shot learning, meta learning fuzzy models, artificial neural networks, and evolutionary algorithms have emerged as promising tools for the development and application of intelligent systems in bioinformatics. Advanced artificial intelligence-based solutions can learn from medical big data and evolve according to changes in the environment by taking into account the uncertainty that characterizes medical data and processes. The use of artificial intelligence in computational healthcare and bioinformatics can improve the management of clinical disease by introducing intelligent solutions for prevention, diagnosis, treatment, and follow-up as well as analysis of administrative processes.

The aim of this Special Issue is to stimulate discussion on the design, use, and evaluation of advanced artificial intelligence-based methods for computational healthcare and bioinformatics in order to leverage deeper insights from the vast amount of medical data for smart healthcare. Although computational intelligence gives an opportunity to delve into the voluminous medical data for machine intelligence, information, and decision-making processes, there are still some unresolved issues such as security and privacy, data trustworthiness and quality, and participation motivation. This interdisciplinary Special Issue invites scientists, industry practitioners and researchers to submit original research and review papers. This Special Issue will comprehensively cover protocols, algorithms, frameworks, and technologies for medical data processing and management.

Potential topics include but are not limited to the following:

• Advanced intelligent medical systems for smart healthcare
• Fuzzy logic and fuzzy models for medical informatics
• Deep learning, deep reinforcement learning, few-shot learning, and meta learning for smart healthcare
• Artificial neural networks for smart healthcare
• Computational intelligence and big data in smart healthcare
• Big medical data processing and management in smart healthcare
• Big data-driven cognitive computing for medical informatics
• Multi-model interfaces in intelligent medical systems
• Deep learning for embedded vision in smart healthcare
• Online stream processing of medical big data for smarter healthcare applications
• Computational intelligence in machine vision and medical mage/signal processing
• Trust, security, privacy and fairness in medical informatics
• Computational intelligence applications in healthcare