Augmented Reality (AR) technology is one of the latest developments and is receiving ever-increasing attention. Many researches are conducted on an international scale in order to study the effectiveness of its use in education. The purpose of this work was to record the characteristics of AR applications, in order to determine the extent to which they can be used effectively for educational purposes and reveal valuable insights. A Systematic Bibliographic Review was carried out on 73 articles. The structure of the paper followed the PRISMA review protocol. Eight questions were formulated and examined in order to gather information about the characteristics of the applications. From 2016 to 2020 the publications studying AR applications were doubled. The majority of them targeted university students, while a very limited number included special education. Physics class and foreign language learning were the ones most often chosen as the field to develop an app. Most of the applications (68.49%) were designed using marker detection technology for the Android operating system (45.21%) and were created with Unity (47.95%) and Vuforia (42.47%) tools. The majority of researches evaluated the effectiveness of the application in a subjective way, using custom-made not valid and reliable tools making the results not comparable. The limited number of participants and the short duration of pilot testing inhibit the generalization of their results. Technical problems and limitations of the equipment used are mentioned as the most frequent obstacles. Not all key-actors were involved in the design and development process of the applications. This suggests that further research is needed to fully understand the potential of AR applications in education and to develop effective evaluation methods. Key aspects for future research studies are proposed.

The objective of the current study is to identify and classify outcome measures currently used for the assessment of rehabilitation or assistive robot devices. We conducted a systematic review of the literature using PubMed, MEDLINE, CIRRIE, and Scopus databases for studies that assessed rehabilitation or assistive robot devices from 1980 through January 2016. In all, 31 articles met all inclusion criteria. Tailor-made questionnaires were the most commonly used tool at 66.7%, while the great majority (93.9%) of the studies used nonvalidated instruments. The study reveals the absence of a standard scale which makes it difficult to compare the results from different researchers. There is a great need, therefore, for a valid and reliable instrument to be available for use by the intended end users for the subjective assessment of robot devices. The study concludes by identifying two scales that have been validated in general assistive technology devices and could support the scope of subjective assessment in rehabilitation or assistive robots (however, with limited coverage) and a new one called PYTHEIA, recently published. The latter intends to close the gap and help researchers and developers to evaluate, assess, and produce products that satisfy the real needs of the end users.

(1) Background: Predictive modeling is becoming increasingly relevant in healthcare, aiding in clinical decision making and improving patient outcomes. However, many of the most potent predictive models, such as deep learning algorithms, are inherently opaque, and their decisions are challenging to interpret. This study addresses this challenge by employing Shapley Additive Explanations (SHAP) to facilitate model interpretability while maintaining prediction accuracy. (2) Methods: We utilized Gradient Boosting Machines (GBMs) to predict patient outcomes in an emergency department setting, with a focus on model transparency to ensure actionable insights. (3) Results: Our analysis identifies “Acuity”, “Hours”, and “Age” as critical predictive features. We provide a detailed exploration of their intricate interactions and effects on the model’s predictions. The SHAP summary plots highlight that “Acuity” has the highest impact on predictions, followed by “Hours” and “Age”. Dependence plots further reveal that higher acuity levels and longer hours are associated with poorer patient outcomes, while age shows a non-linear relationship with outcomes. Additionally, SHAP interaction values uncover that the interaction between “Acuity” and “Hours” significantly influences predictions. (4) Conclusions: We employed force plots for individual-level interpretation, aligning with the current shift toward personalized medicine. This research highlights the potential of combining machine learning’s predictive power with interpretability, providing a promising route concerning a data-driven, evidence-based healthcare future.

The paper presents a user-centric methodology in order to design successful mobile health (mHealth) applications. In addition to the theoretical background, such an example is presented with an application targeting chronic pain. The pain domain was decided due to its significance in many aspects: its complexity, dispersion in the population, the financial burden it causes, etc. The paper presents a step-by-step plan in order to build mobile health applications. Participatory design and interdisciplinarity are only some of the critical issues towards the desired result. In the given example (development of the PainApp), a participatory design was followed with a team of seventeen stakeholders that drove the design and development phases. Three physicians, one behavioral scientist, three IT and UX experts, and ten patients collaborated together to develop the final solution. The several features implemented in the PainApp solution are presented in details. The application is threefold: it supports the management, reporting, and treatment effectiveness monitoring. The paper is giving details on the methodological approach while presenting insights on the actual plan and the steps followed for having a patient-centric solution. Key success factors and barriers to mobile health applications that support the need for such an approach are also presented.

BACKGROUND: The present research aims to identify the application of social media for marketing or communication purposes in healthcare. We studied the opinion of healthcare professionals, organizations and health consumers, trying to identify the c