Digital health technology tools (DHTTs) have the potential to transform health care delivery by enabling new forms of participatory and personalized care that fit into patients’ daily lives. However, realizing this potential requires careful navigation of numerous challenges. This viewpoint presents the authors’ experiences and perspectives on the development and implementation of DHTTs, addressing both established practices and controversial topics. This article offers a practical guide organized into 10 recommendations derived from a multidisciplinary lecture series and associated workshop discussions on “Digital Health and Digital Biomarkers” held at the University of Luxembourg in 2023-2024. Key messages include the need to understand specific health care challenges, form interdisciplinary teams, incorporate patient feedback, select appropriate measurement technologies, ensure data integration and interoperability, apply advanced data science techniques, use scalable designs and open standards, comply with regulatory requirements, and maintain continuous evaluation and improvement. While the guide highlights essential practices, it also addresses contentious issues such as balancing innovation with regulatory compliance, addressing ethical concerns in artificial intelligence adoption, managing privacy versus the need for comprehensive data integration and open science, and managing the financial sustainability of DHTTs. The authors argue that digital health’s greatest potential lies in its ability to provide participatory and personalized care, but this requires a delicate balance between technological advances and ethical, legal, and social implications. Overall, this workshop-derived viewpoint aims to help health care professionals, engineers, developers, and researchers not only adopt best practices but also address and resolve the controversial aspects inherent in the development of DHTTs.

Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of ‘virtual’ and ‘augmented’ contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.

Complex diseases often share genetic susceptibility factors, molecular pathways, and pathological mechanisms. Understanding these commonalities through systematic cross‐disease comparisons can reveal both disease‐specific and shared biomarkers, potentially suggesting new therapeutic targets and opportunities for drug repurposing. In recent years, the growth of multi‐omics datasets across diverse diseases, coupled with advances in computational systems biology, has enabled sophisticated cross‐disease analyses. New methodological frameworks have emerged for integrating and comparing disease‐specific molecular signatures, from gene‐level analyses to complex network‐based approaches. Here, we present a comprehensive framework for computational cross‐disease comparison and integration of omics data, covering established and emerging methodologies. These include gene‐level comparative analyses, pathway‐based approaches, network biology methods, matrix factorization techniques, and machine learning approaches. We examine important aspects of data preprocessing, normalization, and integration, suggesting practical solutions to common technical challenges. We provide a detailed overview of relevant software tools and databases, discussing their strengths, limitations, and optimal use cases for cross‐disease analysis. Finally, we explore current trends in cross‐disease omics analysis, particularly through deep learning methods, highlighting new opportunities for methodological innovation and biological discovery in this field. This compilation of computational methods and practical insights aims to serve as a resource both for bioinformaticians seeking guidance on optimal method selection and biomedical researchers interested in applied cross‐disease analyses. In addition to highlighting practical recommendations and common pitfalls, it provides an entry point to the extensive literature in the field, supporting readers in identifying and further exploring suitable methods for their research needs.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Databases and Expert Systems

Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.