Special Issue "Artificial Intelligence and Big Data in Healthcare"

Dear Colleagues,

Machine learning models can be used in medicine to scan medical data and reveal insights to support health progress results and patient experiences. Artificial intelligence (AI) is promptly becoming a vital aspect of modern healthcare, thanks to recent developments in computer science and informatics. Medical professionals are using AI algorithms and other AI-powered tools to assist them in clinical settings and continuing research. Clinical decision assistance and image analysis are now the most common uses for AI in medical settings. Clinical decision support tools assist doctors in making decisions about treatments, drugs, mental health, and other patient requirements by giving them quick access to pertinent information or research. AI technologies are being utilized in medical imaging to assess CT scans, X-rays, MRIs, and other pictures for lesions or other discoveries that a human radiologist may overlook. Many healthcare institutions throughout the world have been field-testing novel AI-supported solutions, such as algorithms to help monitor patients and AI-powered tools to screen COVID-19 patients, in response to the issues that the COVID-19 pandemic has posed. The study and results of these experiments are still being compiled, and the overall guidelines for using AI in medicine are still being established. However, the number of ways in which AI may help clinicians, researchers, and the patients they serve is continuously growing.

• Time series analysis with medical applications;
• ML systems that assist with evidence-based medicine;
• Algorithms, models, software, and tools in bioinformatics;
• Computer-aided prognostic decision making using ML;
• Medical decision support for treatment and classification using ML;
• Disease diagnosis and prediction systems using ML;
• Medical imaging decision analysis and prediction using ML;
• Intelligent computing platforms for medical decision making using ML;
• Application of AI in medical decision-making systems;
• Medical informatics with AI for healthcare;
• Privacy and security in healthcare data management with AI and cybersecurity;
• AI and ethical computational intelligence for health data management;
• Deep learning for medical image processing and decision making;
• Medical image segmentation and classification with deep learning and computer vision techniques;
• Advances in distributed machine learning for health data management;
• New trends in vision analytics with deep learning and computer vision for medical imaging and data analytics.

Dr. Sathishkumar Easwaramoorthy
Guest Editor

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• data mining
• machine learning
• artificial intelligence
• predictive analytics
• image processing
• natural language processing
• deep learning
• data analytics
• image segmentation