With radiology shortages affecting over half of the global population, the potential of artificial intelligence to revolutionize medical diagnosis and treatment is ever more important. However, lacking trust from medical professionals hinders the widespread adoption of AI models in health sciences. Explainable AI (XAI) aims to increase trust and understanding of black box models by identifying biases and providing transparent explanations. This is the first survey that explores explainable user interfaces (XUI) from a medical domain perspective, analysing the visualization and interaction methods employed in current medical XAI systems. We analysed 42 explainable interfaces following the PRISMA methodology, emphasizing the critical role of effectively conveying information to users as part of the explanation process. We contribute a taxonomy of interface design properties and identify five distinct clusters of research papers. Future research directions include contestability in medical decision support, counterfactual explanations for images, and leveraging Large Language Models to enhance XAI interfaces in healthcare.

The aim of this study is to develop a practical software solution for real-time recognition of sign language words using two arms. This will facilitate communication between hearing-impaired individuals and those who can hear. We are aware of several sign language recognition systems developed using different technologies, including cameras, armbands, and gloves. However, the system we propose in this study stands out for its practicality, utilizing surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. We address the drawbacks of other methods, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have limited their practical application. Our software can run on different operating systems using digital signal processing and machine learning methods specific to this study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The random forest algorithm emerged as the most successful, achieving a remarkable 99.875% accuracy, while the naïve Bayes algorithm had the lowest success rate with 87.625% accuracy. The new system promises to significantly improve communication for people with hearing disabilities and ensures seamless integration into daily life without compromising user comfort or lifestyle quality.

OBJECTIVE: This study investigates the potential utility of augmented reality (AR) in the endoscopic transsphenoidal approach (TSA). While previous research has addressed technical challenges in AR for TSA, this paper explores how design factors can improve AR for neurosurgeons from a human-centred design perspective.
METHODS: Preliminary qualitative research involved observations of TSA procedures ( n = 2 ) and semi-structured interviews with neurosurgeons ( n = 4 ). These informed the design of an AR mockup, which was evaluated with neurosurgeons ( n = 3 ). An interactive low-fidelity prototype-the \"AR-assisted Navigation for the TransSphenoidal Approach (ANTSA)\"-was then developed in Unity 3D. A user study ( n = 4 ) evaluated the low-fidelity prototype of ANTSA through contextual interviews, providing feedback on design factors.
RESULTS: AR visualisations may be beneficial in streamlining the sellar phase and reducing intraoperative errors such as excessive or inadequate exposure. Key design recommendations include a lean mesh rendering, an intuitive colour palette, and optional structure highlighting.
CONCLUSIONS: This research presents user-centred design guidelines to improve sensemaking and surgical workflow in the sellar phase of TSA, with the goal of improving clinical outcomes. The specific improvements that AR could bring to the workflow are discussed along with surgeons\' reservations and its possible application towards training less experienced physicians.

Chatbots can effect large-scale behaviour change because they are accessible through social media, flexible, scalable, and gather data automatically. Yet research on the feasibility and effectiveness of chatbot-administered behaviour change interventions is sparse. The effectiveness of established behaviour change interventions when implemented in chatbots is not guaranteed, given the unique human-machine interaction dynamics. We pilot-tested chatbot-based behaviour change through information provision and embedded animations. We evaluated whether the chatbot could increase understanding and intentions to adopt protective behaviours during the pandemic. Fifty-nine culturally and linguistically diverse participants received a compassion intervention, an exponential growth intervention, or no intervention. We measured participants\' COVID-19 testing intentions and measured their staying-home attitudes before and after their chatbot interaction. We found reduced uncertainty about protective behaviours. The exponential growth intervention increased participants\' testing intentions. This study provides preliminary evidence that chatbots can spark behaviour change, with applications in diverse and underrepresented groups.

UNASSIGNED: In recent years, the scientific community has been captivated by the intriguing Autonomous sensory meridian response (ASMR), a unique phenomenon characterized by tingling sensations originating from the scalp and propagating down the spine. While anecdotal evidence suggests the therapeutic potential of ASMR, the field has witnessed a surge of scientific interest, particularly through the use of neuroimaging techniques including functional magnetic resonance imaging (fMRI) as well as electroencephalography (EEG) and physiological measures such as eye tracking (Pupil Diameter), heart rate (HR), heartbeat-evoked potential (HEP), blood pressure (BP), pulse rates (PR), finger photoplethysmography (PPG), and skin conductance (SC). This article is intended to provide a comprehensive overview of technology\'s contributions to the scientific elucidation of ASMR mechanisms.
UNASSIGNED: A meticulous literature review was undertaken to identify studies that have examined ASMR using EEG and physiological measurements. The comprehensive search was conducted across databases such as PUBMED, SCOPUS, and IEEE, using a range of relevant keywords such as \'ASMR\', \'Autonomous sensory meridian response\', \'EEG\', \'fMRI\', \'electroencephalography\', \'physiological measures\', \'heart rate\', \'skin conductance\', and \'eye tracking\'. This rigorous process yielded a substantial number of 63 PUBMED and 166 SCOPUS-related articles, ensuring the inclusion of a wide range of high-quality research in this review.
UNASSIGNED: The review uncovered a body of research utilizing EEG and physiological measures to explore ASMR\'s effects. EEG studies have revealed distinct patterns of brain activity associated with ASMR experiences, particularly in regions implicated in emotional processing and sensory integration. In physiological measurements, a decrease in HR and an increase in SC and pupil diameter indicate relaxation and increased attention during ASMR-triggered stimuli.
UNASSIGNED: The findings of this review underscore the significance of EEG and physiological measures in unraveling the psychological and physiological effects of ASMR. ASMR experiences have been associated with unique neural signatures, while physiological measures provide valuable insights into the autonomic responses elicited by ASMR stimuli. This review not only highlights the interdisciplinary nature of ASMR research but also emphasizes the need for further investigation to elucidate the mechanisms underlying ASMR and explore its potential therapeutic applications, thereby paving the way for the development of novel therapeutic interventions.

This study compares how English-speaking adults and children from the United States adapt their speech when talking to a real person and a smart speaker (Amazon Alexa) in a psycholinguistic experiment. Overall, participants produced more effortful speech when talking to a device (longer duration and higher pitch). These differences also varied by age: children produced even higher pitch in device-directed speech, suggesting a stronger expectation to be misunderstood by the system. In support of this, we see that after a staged recognition error by the device, children increased pitch even more. Furthermore, both adults and children displayed the same degree of variation in their responses for whether \"Alexa seems like a real person or not\", further indicating that children\'s conceptualization of the system\'s competence shaped their register adjustments, rather than an increased anthropomorphism response. This work speaks to models on the mechanisms underlying speech production, and human-computer interaction frameworks, providing support for routinized theories of spoken interaction with technology.

The release of GPT-4 has garnered widespread attention across various fields, signaling the impending widespread adoption and application of Large Language Models (LLMs). However, previous research has predominantly focused on the technical principles of ChatGPT and its social impact, overlooking its effects on human-computer interaction and user psychology. This paper explores the multifaceted impacts of ChatGPT on human-computer interaction, psychology, and society through a literature review. The author investigates ChatGPT\'s technical foundation, including its Transformer architecture and RLHF (Reinforcement Learning from Human Feedback) process, enabling it to generate human-like responses. In terms of human-computer interaction, the author studies the significant improvements GPT models bring to conversational interfaces. The analysis extends to psychological impacts, weighing the potential of ChatGPT to mimic human empathy and support learning against the risks of reduced interpersonal connections. In the commercial and social domains, the paper discusses the applications of ChatGPT in customer service and social services, highlighting the improvements in efficiency and challenges such as privacy issues. Finally, the author offers predictions and recommendations for ChatGPT\'s future development directions and its impact on social relationships.

This article, from the perspective of structural features, focuses on in-car user interface icons and explores the impact of different icon structural features on visual search efficiency. Initially, we categorised the icons into four groups based on structural features: individual structure icons (ISI), enclosed structure icons (ESI), horizontal structure icons (HSI) and vertical structure icons (VSI). Subsequently, we conducted a visual search experiment with structure as the sole variable, recording participants\' behaviours and eye-tracking data. Finally, data analysis was conducted using methods including analysis of variance and logistic regression. The results indicate that differences in icon structural features significantly affect visual search efficiency, showcasing significant intergroup differences. HSI exhibit the highest visual search efficiency, while ESI show the lowest efficiency. ISI have shorter response times but the lowest matching accuracy. VSI only perform better than ESI. These findings hold significant implications for optimising icon design and enhancing visual search efficiency.
Visual search efficiency of icons is crucial for human-computer interaction. We investigated how the structural features of icons influence visual search efficiency. Horizontal icons are most effective, enclosed icons the least. Individual icons are quick but less accurate. Vertical icons outperform enclosed ones. Structural features should be considered in design.

Human Activity Recognition (HAR) plays an important role in the automation of various tasks related to activity tracking in such areas as healthcare and eldercare (telerehabilitation, telemonitoring), security, ergonomics, entertainment (fitness, sports promotion, human-computer interaction, video games), and intelligent environments. This paper tackles the problem of real-time recognition and repetition counting of 12 types of exercises performed during athletic workouts. Our approach is based on the deep neural network model fed by the signal from a 9-axis motion sensor (IMU) placed on the chest. The model can be run on mobile platforms (iOS, Android). We discuss design requirements for the system and their impact on data collection protocols. We present architecture based on an encoder pretrained with contrastive learning. Compared to end-to-end training, the presented approach significantly improves the developed model\'s quality in terms of accuracy (F1 score, MAPE) and robustness (false-positive rate) during background activity. We make the AIDLAB-HAR dataset publicly available to encourage further research.

To understand the alignment between reasonings of humans and artificial intelligence (AI) models, this empirical study compared the human text classification performance and explainability with a traditional machine learning (ML) model and large language model (LLM). A domain-specific noisy textual dataset of 204 injury narratives had to be classified into 6 cause-of-injury codes. The narratives varied in terms of complexity and ease of categorization based on the distinctive nature of cause-of-injury code. The user study involved 51 participants whose eye-tracking data was recorded while they performed the text classification task. While the ML model was trained on 120,000 pre-labelled injury narratives, LLM and humans did not receive any specialized training. The explainability of different approaches was compared based on the top words they used for making classification decision. These words were identified using eye-tracking for humans, explainable AI approach LIME for ML model, and prompts for LLM. The classification performance of ML model was observed to be relatively better than zero-shot LLM and non-expert humans, overall, and particularly for narratives with high complexity and difficult categorization. The top-3 predictive words used by ML and LLM for classification agreed with humans to a greater extent as compared to later predictive words.