Abstract
Introduction: Emergency department visits have increased substantially, leading to a significant rise in waiting time for patients. Several kiosk-based solutions have been introduced to reduce waiting times in healthcare facilities and to increase efficacy and user satisfaction. Purpose of the Study: This systematic review aims to identify the most effective self-service kiosk features for collecting patients' health information and to evaluate their acceptability among elderly and less educated populations, despite not being the focus, there is pontencial in the development of the system interface to facilitate the perception and understanding of those with less digital literacy. Methods: We conducted a systematic review of studies on diagnosis, replacement of face-to-face consultation, and triage kiosks published between January 2009 and March 2023 in the databases PubMed, IEEE Xplore, Web of Science, Cochrane Library, ScienceDirect, and Scopus. Results: The eight analyzed studies included 2,298 participants in total, with participants aged between 16 and 94 years. Most studies provided kiosk assistance. Elderly patients demonstrated the capability and willingness to participate in technological interventions. Conclusion: User interface elements were the most critical features in health kiosk design, followed by clear communication and patients' understanding of the benefits associated with kiosk use. The high levels of kiosk acceptance and satisfaction observed indicate a significant opportunity for the introduction of self-service kiosks in various healthcare contexts.

Abstract
Clinical decision support systems have been increasingly utilized in the healthcare industry to improve patient outcomes and enhance clinical decision-making, taking advantage of the growing digital medical data. Despite their potential, there are still obstacles in an extensive adoption of these systems, such as low usability and human factors. In this systematic review, several articles describing clinical decision support systems with clinical validation are used to address some of the gaps, as well as to map the current academic landscape for the given context. The selected articles are observed through a Human-Computer Interaction perspective, aiming to identify the state-of-the-art, as well as barriers to the application of these principles. From an initial database search resulting in 121 articles, 16 articles were selected that fulfilled the chosen criteria: (1) article must be available and written in English, (2) article must report experimental work, (3) the reported system must be clinically validated. The research strategy followed the PRISMA framework. We highlight the need for clinical validation, a standardized clinical decision support taxonomy and the evaluation of these tools across multiple variables. Based on the found results, a list of recommendations can be formed to aid the development of future CDSS, or the improvement of current ones.

Abstract
Postoperative residual neuromuscular block (PRNB) is still a problem during the surgery procedures resulting in health problems, such as, airway obstruction, hypoxia and pulmonary aspiration. To perform more accurate monitoring of the patient during surgery quantitative neuromuscular blockade monitoring measuring TOF ratio has been recommended by medical institutions. There are some devices available using different techniques, however there are only a few number of clinicians using them, since those devices are costly and have difficult clinical set-up. This paper presents a systematic review of current devices for quantitative neuromuscular monitoring during the surgery procedure following the PRISMA methodology. This study was carried out to list the currently available devices and report the capabilities that are missing in these devices since 2017. The databases used to do the research were PubMed, Cochrane Library, PubMed Central (PMC), Web of Science, IEEE Xplore, ScienceDirect, Directory of Open Access Journals (DOAJ). 17 articles were selected, presenting comparisons between two devices using different techniques. Quantitative monitoring provides the most accurate TOF ratio measurement but still needs to be incentivized.

Abstract
Background: Schizophrenia is the most common psychotic illness in the world. The negative and cognitive symptoms of this mental disorder often prevent full reintegration of patients into society, and cannot be effectively addressed with drugs alone, relying on therapy and rehabilitation. Video games as a digital tool for rehabilitation and therapy can help promote accessibility, improve patient engagement and reduce costs to institutions. Methods: A systematic review was conducted from October to November 2022 to analyze the effects of video game based rehabilitation and therapy on negative and cognitive symptoms in schizophrenic patients. The databases used to perform the search were Scopus, PubMed and Web of Science, with the search query: Schizophrenia AND (Video Game OR Serious Game). Results: A total of 228 papers were found, of which 88 duplicates were removed. After reading the titles and abstracts of the remaining 140 papers, 116 were excluded for not meeting the defined eligibility criteria for the review. Of the 24 papers left, 20 were excluded for similar reasons, resulting in the inclusion of four studies in this systematic review Conclusion: The available data for this review was limited, highlighting a need for more research in the field as well as standardization of terms used to describe the digital tools developed and assessment methods used to gather results from these interventions. Nevertheless, statistical data from the four studies included in this review showed that serious games are a promising tool for the rehabilitation and therapy of negative and cognitive symptoms of schizophrenic patients, with significant effects on the patients' performance and motivation.

Abstract
Background-Classification, detection, and segmentation of minimally invasive instruments is an essential component for robotic-assisted surgeries and surgical skill assessments. Methods-Cochrane Library, PubMed, ScienceDirect, and IEEE Xplore databases were searched from January 2018 to May 2022. Selected studies evaluated deep learning (DL) models for image and video analysis of laparoscopic surgery. Comparisons were made of the studies' characteristics such as the dataset source, type of laparoscopic operation, number of images/videos, and types of neural networks (NN) used. Results-22 out of 152 studies identified met the selection criteria. The application with the greatest number of studies was instrument detection (59.1%) and the second was instrument segmentation (40.9%). The most tested procedure was cholecystectomy (72.73%). Conclusions-Although CNN-based algorithms outperform other methods in instrument detection and many have been proposed, there are still challenging conditions where numerous difficulties arise. U-Nets are the dominant force in the field for segmentation, but other models such as Mask R-CNN follow close behind with comparable results. Deep learning holds immense potential in laparoscopic surgery and many improvements are expected as soon as data quality improves.