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Artificial intelligence (AI) is increasingly applied in medicine, including clinical decision-making. AI-based decision support systems (DSS) can enhance early risk detection and treatment optimization. However, the perspectives of patients and their support persons on AI-assisted DSS in clinical care, particularly regarding shared decision-making (SDM), remain underexplored. This study investigates the expectations, informational needs, and perceptions of patients who underwent kidney transplantation and their support persons regarding AI-assisted DSS and its influence on SDM in posttransplant care. In a longitudinal qualitative study, 36 semistructured interviews were conducted with patients who underwent kidney transplantation and their support persons at a German kidney transplant center. Participants were asked about their views on AI's role in follow-up care, its impact on communication, trust, and decision-making, as well as their informational needs regarding AI-assisted DSS. Interviews were transcribed, pseudonymized, and analyzed using framework analysis. Participants recognized AI's potential to support clinicians by identifying risks of transplant loss, rejection, and infection, and by providing data-driven treatment recommendations. However, they emphasized that final decisions should remain with physicians. A majority of participants (n=28, 78%) expressed concern that AI might depersonalize care and diminish physician-patient communication due to a lack of \"human touch.\" Participants demonstrated limited understanding of AI-based DSS functionality and highlighted the need for simple, accessible educational materials (eg, leaflets) explaining AI operations. While most doubted AI could replicate human empathy, some acknowledged that AI might be perceived as more attentive than time-pressured physicians, offering consistent monitoring and support. Participants consistently stressed that AI should augment, not replace, clinical decision-making. Patients who underwent kidney transplantation and support persons endorse the integration of AI in follow-up care when it enhances clinical decision-making without supplanting the physician's role. Acceptance and trust depend on transparency, accountability, and preserving the \"human touch\" in care. The development of educational tools to communicate AI functions and limitations is crucial to empower patients and support persons in SDM processes and to ensure AI complements, rather than undermines, patient-centered care.

Turbulent superstructures in horizontally extended three-dimensional Rayleigh–Bénard convection flows are investigated in controlled laboratory experiments in water at Prandtl number ${Pr}=7$. A Rayleigh–Bénard cell with square cross-section, aspect ratio $\\varGamma =l/h=25$, side length $l$ and height $h$ is used. Three different Rayleigh numbers in the range $10^{5} < {Ra} < 10^{6}$ are considered. The cell is accessible optically, such that thermochromic liquid crystals can be seeded as tracer particles to monitor simultaneously temperature and velocity fields in a large section of the horizontal mid-plane for long time periods of up to 6 h, corresponding to approximately $10^{4}$ convective free-fall time units. The joint application of stereoscopic particle image velocimetry and thermometry opens the possibility to assess the local convective heat flux fields in the bulk of the convection cell and thus to analyse the characteristic large-scale transport patterns in the flow. A direct comparison with existing direct numerical simulation data in the same parameter range of $Pr$, ${Ra}$ and $\\varGamma$ reveals the same superstructure patterns and global turbulent heat transfer scaling ${Nu}({Ra})$. Slight quantitative differences can be traced back to violations of the isothermal boundary condition at the extended water-cooled glass plate at the top. The characteristic scales of the patterns fall into the same size range, but are systematically larger. It is confirmed experimentally that the superstructure patterns are an important backbone of the heat transfer. The present experiments enable, furthermore, the study of the gradual evolution of the large-scale patterns in time, which is challenging in simulations of large-aspect-ratio turbulent convection.

Artificial intelligence (AI) has emerged as a promising tool to enhance medical practice and improve patient outcomes. However, introducing AI in interactions between patients, support persons (SPs) and physicians may create real or perceived information asymmetries and may not always be well accepted by end-users. To ensure that AI contributes to patient empowerment rather than undermining it, there is a need to better understand how AI-based tools affect communication, trust and decision-making in clinical encounters. Research should focus on identifying how AI can support patients’ autonomy, trust and acceptance, how it may strengthen the role of SPs and promote transparent and ethically sound care. With these findings, applying a human-centered design with established technology acceptance frameworks (e.g. TAM, UTAUT) will be crucial to guide evidence-based implementation. Only by involving patients, SPs and physicians in AI development can these technologies unfold their full potential to deliver equitable, interpretable and patient-centered healthcare.

As working and learning environments become open and flexible, people are also potentially surrounded by ambient noise, which causes an increase in mental workload. The present study uses electroencephalogram (EEG) and subjective measures to investigate if noise-canceling technologies can fade out external distractions and free up mental resources. Therefore, participants had to solve spoken arithmetic tasks that were read out via headphones in three sound environments: a quiet environment ( no noise ), a noisy environment ( noise ), and a noisy environment but with active noise-canceling headphones ( noise-canceling ). Our results of brain activity partially confirm an assumed lower mental load in no noise and noise-canceling compared to noise test condition. The mean P300 activation at Cz resulted in a significant differentiation between the no noise and the other two test conditions. Subjective data indicate an improved situation for the participants when using the noise-canceling technology compared to “normal” headphones but shows no significant discrimination. The present results provide a foundation for further investigations into the relationship between noise-canceling technology and mental workload. Additionally, we give recommendations for an adaptation of the test design for future studies.

Introduction When in need of emergency care and ambulance services, the ambulance nurse is often the first point of contact for the patient with healthcare. This role requires comprehensive knowledge of the ambulance nurse to be able to assign the right level of care and, if necessary, to provide self-care advice for patients with no further conveyance to hospital. Recently, an application was developed for transmitting real-time video to facilitate consultation between ambulance nurses and prehospital physicians in the role of regional medical support (RMS) for ambulance care. The use of video communication as a complement of medical support when referring to self-care is still an unexplored method in a prehospital setting. Our study aimed to elucidate ambulance nurses’ experience of video consultation with RMS physician during the assessment of patients considered to be triaged to self-care. Method We conducted a qualitative design study using semi-structured interviews with open questions. Twelve ambulance nurses were included in the study. To explore the ambulance nurses’ experience of performing video consultation with RMS physician, in cases when a patient was assessed and triaged to self-care, a content analysis was performed. Results A main category emerged from the results: “ Video consultation as decision support in the ambulance care promotes increased patient participation and for the ambulance nurses, it creates a feeling of increased patient safety “. The main category was based and formed on the following categories: “ Simultaneous presence of ambulance nurse and a physician increases patient participation during the assessment resulting in a confident care decision “. “Interprofessional collaboration strengthens the medical assessment”. “Video technology promotes accessibility for patients needs in the ambulance care regardless of emergency level”. Conclusions Ambulance nurses experienced that the use of video consultation increases patient involvement and confidence in healthcare when both the ambulance nurse and the physician were present when deciding on self-care advice. The live imaging allowed the ambulance nurse and prehospital physician to reach a consensus on the patient’s current medical care needs, which in turn led to a feeling of increased patient safety for the ambulance nurses.

The number of hip fractures per year worldwide is estimated to reach 6 million by the year 2050. Despite the many advantages of regional blockades when managing pain from such a fracture, these are used to a lesser extent than general analgesia. One reason is that the opportunities for training and obtaining clinical experience in applying nerve blocks can be a challenge in many clinical settings. Ultrasound image guidance based on artificial intelligence may be one way to increase nerve block success rate. We propose an approach using a deep learning semantic segmentation model with U-net architecture to identify the femoral nerve in ultrasound images. The dataset consisted of 1410 ultrasound images that were collected from 48 patients. The images were manually annotated by a clinical professional and a segmentation model was trained. After training the model for 350 epochs, the results were validated with a 10-fold cross-validation. This showed a mean Intersection over Union of 74%, with an interquartile range of 0.66–0.81.

Stretch-bending straightening is used to ensure the desired flatness properties of packaging steel in the final stage of semi-finished product manufacturing. Not only is the flatness of the steel affected by the alternating bending load and the tensile load during the stretch-bending straightening process, but the mechanical properties also change depending on several factors. It was found out that the stretch-bending straightening parameters, the temper-rolling degree and the amount of interstitial elements have an influence on this change in mechanical properties. A follow-up ageing process, after stretch-bending straightening, also has a significant impact on this change. Based on these observations, a multivariate prediction model is developed describing the dependence between straightening parameters and resulting yield strength characteristics in non-aged and aged conditions for three different packaging steels.

Nowadays, smartphones are equipped with various sensors collecting a huge amount of sensitive personal information about their users. However, for smartphone users, it remains hidden, and sensitive information is accessed by used applications and data requesters. Moreover, governmental institutions have no means to verify if applications requesting sensitive informa-tion are compliant with the General Data Protection Directive (GDPR), as it is infeasible to check the technical details and data requested by applications that are on the market. Thus, this research aims to shed light on the compliance analysis of applications with the GDPR. Therefore, a multidimensional analysis is applied to analyzing the permission requests of applications and empirically test if the information provided about potentially dangerous permissions influences the privacy awareness and their willingness to pay or sell personal data of users. The use case of Google Maps has been chosen to examine privacy awareness and the monetary assessment of data in a concrete scenario. The information about the multidimensional analysis of the permission requests of Google Maps and the privacy consent form is used to design privacy nudges to inform users about potentially harmful permission requests that are not in line with the GDPR. The privacy nudges are evaluated in two crowdsourcing experiments with overall 426 participants, showing that information about harmful data collection practices increases privacy awareness and also the willingness to pay for the protection of personal data.

Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make sustainable progress. What does it take to implement a clinical decision support system, what makes it usable for the domain experts, and what brings it eventually into practical usage? So far, there has been little research to answer these questions. This work presents a multidisciplinary view of machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. The target audience is computer scientists, who plan to do research in a clinical context. The paper starts from a relatively straightforward risk prediction system in the subspecialty nephrology that was evaluated on historic patient data both intrinsically and based on a reader study with medical doctors. Although the results were quite promising, the focus of this article is not on the model itself or potential performance improvements. Instead, we want to let other researchers participate in the lessons we have learned and the insights we have gained when implementing and evaluating our system in a clinical setting within a highly interdisciplinary pilot project in the cooperation of computer scientists, medical doctors, ethicists, and legal experts.