This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.

Teleoperated medical robotic systems allow procedures such as surgeries, treatments, and diagnoses to be conducted across short or long distances while utilizing wired and/or wireless communication networks. This study presents a systematic review of the relevant literature between the years 2004 and 2015, focusing on medical teleoperated robotic systems which have witnessed tremendous growth over the examined period. A thorough insight of telerobotics systems discussing design concepts, enabling technologies (namely robotic manipulation, telecommunications, and vision systems), and potential applications in clinical practice is provided, while existing limitations and future trends are also highlighted. A representative paradigm of the short-distance case is the da Vinci Surgical System which is described in order to highlight relevant issues. The long-distance telerobotics concept is exemplified through a case study on diagnostic ultrasound scanning. Moreover, the present review provides a classification into short- and long-distance telerobotic systems, depending on the distance from which they are operated. Telerobotic systems are further categorized with respect to their application field. For the reviewed systems are also examined their engineering characteristics and the employed robotics technology. The current status of the field, its significance, the potential, as well as the challenges that lie ahead are thoroughly discussed.

As an integral part of patient care, nursing is required to constantly adapt to changes in the healthcare system, as well as the wider financial and societal environment. Among the key factors driving these changes is the aging of population. Combined with an existing shortage of nursing and caregiving professionals, accommodating for the patients and elderly needs within hospitals, elderly-care facilities and at a home setting, becomes a societal challenge. Amongst the technological solutions that have evolved in response to these developments, nursing and assistive robotics claim a pivotal role. The objective of the present study is to provide an overview of today's landscape in nursing and assistive robotics, highlighting the benefits associated with adopting such solutions in standard clinical practice. At the same time, to identify existing challenges and limitations that essentially outline the area's future directions. Beyond technological innovation, the manuscript also investigates the end-users' angle, being a crucial parameter in the success of robotics solutions operating within a healthcare environment. In this direction, the results of a survey designed to capture the nursing professionals' perspective toward more informed robotics design and development are presented.

Medication non-adherence (MNA) constitutes a complex health problem contributing to increased economic burden and poor health outcomes. The Medication Adherence Model (MAM) supports that numerous processes are involved in medication adherence (MA). Based on the MAM and guidelines of the World Health Organization (WHO), this scoping review aimed to identify the barriers and facilitators associated with MA, and the behavioral health interventions and techniques among chronic conditions presenting with high non-adherence rates (asthma, cancer, diabetes, epilepsy, HIV/AIDS, and hypertension). PubMed, PsycINFO, and Scopus databases were screened, and 243 studies were included. A mixed methods approach was used to collate the evidence and interpret findings. The most commonly reported barriers to MA across conditions were younger age, low education, low income, high medication cost, side effects, patient beliefs/perceptions, comorbidities, and poor patient–provider communication. Additionally, digitally delivered interventions including components such as medication and condition education, motivational interviewing (MI), and reinforcement and motivational messages led to improvements in MA. This review highlights the importance of administrating multicomponent interventions digitally and personalized to the patients’ individual needs and characteristics, responding to the adherence barriers faced. This is the first review examining and synthesizing evidence on barriers and facilitators to MA and behavioral health interventions used for improving MA across chronic conditions with the highest non-adherence rates and providing recommendations to researchers and clinicians. Stakeholders are called to explore methods overcoming barriers identified and developing effective multicomponent interventions that can reduce the high rates of MNA.

Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.

We propose a unifying framework for efficient encoding, transmission, and quality assessment of atherosclerotic plaque ultrasound video. The approach is based on a spatially varying encoding scheme, where video-slice quantization parameters are varied as a function of diagnostic significance. Video slices are automatically set based on a segmentation algorithm. They are then encoded using a modified version of H.264/AVC flexible macroblock ordering (FMO) technique that allows variable quality slice encoding and redundant slices (RSs) for resilience over error-prone transmission channels. We evaluate our scheme on a representative collection of ten ultrasound videos of the carotid artery for packet loss rates up to 30%. Extensive simulations incorporating three FMO encoding methods, different quantization parameters, and different packet loss scenarios are investigated. Quality assessment is based on a new clinical rating system that provides independent evaluations of the different parts of the video (subjective). We also use objective video-quality assessment metrics and estimate their correlation to the clinical quality assessment of plaque type. We find that some objective quality assessment measures computed over the plaque video slices gave very good correlations to mean opinion scores (MOSs). Here, MOSs were computed using two medical experts. Experimental results show that the proposed method achieves enhanced performance in noisy environments, while at the same time achieving significant bandwidth demands reductions, providing transmission over 3G (and beyond) wireless networks.