Artificial intelligence has grown extensively in recent times and is changing the healthcare industry from many perspectives: clinical diagnosis, suggested treatment and follow up. Clinical Decision Support (CDS) is a major topic of AI in medicine which assists clinicians at point of care. Existing techniques used for processing health data can be broadly classified into two categories: (a) non-Artificial Intelligence (AI) systems and (b) Artificial Intelligence systems. Even though non-AI techniques are less complex in nature, most of the systems suffer from the drawbacks of inaccuracy and lack of convergence. Hence, these systems are generally replaced by AI-based systems which are much superior to the conventional systems. AI techniques are mostly hybrid in nature and include Artificial Neural Networks (ANN), fuzzy theory, and evolutionary algorithms. Though most of the techniques are theoretically sound, the potential of these techniques is not fully explored for practical applications. Many of the computational applications still depend on non-AI systems, which limit their practical usage.

CDS can be Artificial Intelligence-based, where the AI areas involved are inference and logics and non-Artificial Intelligence-based, where machine learning is used. CDS can support all aspects of clinical tasks, but, to be effective, it must be properly integrated within the clinical workflow, as well as with health records. A typical application of CDS is a Computer Aided Diagnosis (CAD) to assist doctors in the interpretation of medical images. CAD involves, not only AI, but also Computer Vision, Signal Processing and specific medical aspects. CADs find application in breast cancer, lung cancer, colon cancer, coronary artery disease, Alzheimer’s disease and many others.

The goal of this Research Topic is to publish original manuscripts that address broad challenges on both theoretical and application aspects of AI in eHealth, biomedical, health informatics, and medical image analysis. The development of medical artificial intelligence has been related to the development of AI programs intended to help the clinician in the formulation of a diagnosis, the making of therapeutic decisions and the prediction of outcome. This Research Topic provides an opportunity to scholars and researchers to contribute original research articles as well as review articles that will stimulate the continuing effort on the application of AI approaches to solve eHealth and medical problems.

The topics of interest in this Research Topic include, but are not limited to:
• Applications of AI in eHealth
• Knowledge Management of Medical Data
• Evolutionary algorithms for optimization methodologies for eHealth applications
• Data Mining and Knowledge Discovery in Medicine
• Medical Expert Systems
• Personal medical feature data
• Medical device technologies
• Machine learning and deep learning based medical system
• Pattern Recognition in Medicine
• Ambient Intelligence and Pervasive Computing in Medicine and Health Care
• Brain-computer interfaces
• Biological and clinical medicine
• Behavioral, Environmental, and Public health informatics
• Biological network modeling and analysis
• Biomedical imaging and data visualization
• Intelligent medical information systems
• Virtual and augmented reality

The authors are encouraged greatly to submit data or any supplementary material with each article, and possible experiences-feedback reported by physicians and medical staff since these may add important value in terms of increasing visibility, and citations. Also, negative results are awaited since they will contribute to the understanding of the current and future potential of Artificial Intelligence, particularly for eHealth.

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Frontiers in Public Health

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