The known challenge of underutilization of data and biological material from biorepositories as potential resources for medical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability—entitled FAIR Principles (Findability, Accessibility, Interoperability, and Reusability)—are likely to address only parts of the problem. In this article, we argue that biological material and data should be viewed as a unified resource. This approach would facilitate access to complete provenance information, which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for optimization of long-term storage strategies, as demonstrated in the case of biobanks. We propose an extension of the FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human material and data. These FAIR-Health principles should then be applied to both the biological material and data. We also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of volume and breadth of medical data generation, as well as the associated need to process the data efficiently.

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The new era of artificial intelligence (AI) has introduced revolutionary data-driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision-support systems. These advances have created unprecedented momentum in computational medical imaging applications and have given rise to new precision medicine research areas. Radiogenomics is a novel research field focusing on establishing associations between radiological features and genomic or molecular expression in order to shed light on the underlying disease mechanisms and enhance diagnostic procedures towards personalized medicine. The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications. The main deep learning radiogenomics architectures, together with the clinical questions addressed, and the achieved genetic or molecular correlations are presented, while a performance comparison of the proposed methodologies is conducted. Finally, current limitations, potentially understudied topics and future research directions are discussed. Contents 1. Introduction 2. Research methodology 3. Deep architectures used in current radiogenomics studies 4. Clinical applications of deep learning-based radiogenomics 5. Limitations of radiogenomic research 6. Discussion and future directions

Developments in information and communication technology have changed the way healthcare processes are experienced by both patients and healthcare professionals: more and more services are now available through computers and mobile devices. Smartphones are becoming useful tools for managing one’s health, and today, there are many available apps meant to increase self-management, empowerment and quality of life. However, there are concerns about the implications of using mHealth and apps: data protection issues, concerns about sharing information online, and the patients’ capacity for discerning effective and valid apps from useless ones. The new General Data Protection Regulation has been introduced in order to give uniformity to data protection regulations among European countries but shared guidelines for mHealth are yet to develop. A unified perspective across Europe would increase the control over mHealth exploitation, making it possible to think of mHealth as effective and standard tools for future medical practice.

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Chronic inflammatory diseases like periodontitis have a complex pathogenesis and a multifactorial etiology, involving complex interactions between multiple genetic loci and infectious agents. We aimed to investigate the influence of genetic polymorphisms and bacteria on chronic periodontitis risk. We determined the prevalence of 12 single-nucleotide polymorphisms (SNPs) in immune response candidate genes and 7 bacterial species of potential relevance to periodontitis etiology, in chronic periodontitis patients and non-periodontitis control individuals (N = 385). Using decision tree analysis, we identified the presence of bacterial species Tannerella forsythia, Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and SNPs TNF -857 and IL-1A -889 as discriminators between periodontitis and non-periodontitis. The model reached an accuracy of 80%, sensitivity of 85%, specificity of 73%, and AUC of 73%. This pilot study shows that, on the basis of 3 periodontal pathogens and SNPs, patterns may be recognized to identify patients at risk for periodontitis. Modern bioinformatics tools are valuable in modeling the multifactorial and complex nature of periodontitis.

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