Artificial neural networks provide a powerful tool to help doctors analyze, model, and make sense of complex clinical data across a broad range of medical applications. Most applications of artificial neural networks in medicine are classification problems. An artificial neural network (ANN) is a computational model that attempts to account for the parallel nature of the human brain. It is a network of highly interconnected processing elements (neurons) operating in parallel. These elements are inspired by biological nervous systems.

Medical diagnosis using artificial intelligence (AI) systems, particularly artificial neural networks and computer-aided diagnosis with deep learning, is currently a very active research area in medicine and it is believed that it will be more widely used in biomedical systems. Evolving neural network techniques for medical diagnosis are broadly considered since they are ideal in recognizing diseases using scans. Neural networks learn by example so the details of how to recognize the disease are not needed. For instance, the utilization of deep learning-based ANN models aids in the timely diagnosis of gastric cancer with sensitivity and specificity. Advances in deep learning-based ANN models achieve efficacy, accuracy, and reliability in diagnosis. The utilization of other evolving technologies in this field is key for better diagnosis, having a significant impact on preventive measures and treatment. Despite the great benefits of novel technologies in healthcare, patients need protection from defective diagnoses to create a promising future in medical applications within society.

This Special Issue calls for original research and review articles covering artificial intelligence, deep learning, ANN, and other evolving techniques for the diagnosis of diseases.

Potential topics include but are not limited to the following:

• Evolving deep learning-based ANN techniques in diagnosis and prognosis of the disease in real-time
• Hybrid AI models for breast cancer metastasis detection with precision
• Adaptive deep ensemble learning methods in cancer diagnosis for optimal performance
• Deep neural network-based decision support systems for intelligent cardiovascular diseases diagnosis
• Advances in deep neural network and machine learning models for diagnosis of kidney diseases
• Deep learning-based computer-aided diagnosis and imaging markers tools for liver cancer
• Challenges and applications of deep learning neural network techniques in diagnosing skin diseases
• Novel prediction models and analysis in real-time for pancreatic cancer and targeted therapy
• Computational intelligence approaches in the diagnosis of thyroid cancer diseases
• Development and validations of new technologies in diagnosis and grading of prostate cancer
• Machine learning-based histopathologic cancer detection and uncertainty mitigation strategies
• Emerging measurements and improvements in microRNA for cancer screening based on deep neural networks
• Technological advancements in DNA-methylation for early detection and management of cancer
• Deep learning-based single-cell RNA-sequencing analysis tools and their applications in cancer