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BACKGROUND: The field of robotic-assisted surgery is rapidly growing as many robotic surgical devices are in development and about to enter the market. Currently, there is no universally accepted language for labeling the different robotic systems. To facilitate this communication, we created what is, to our knowledge, the first classification of surgical robotic technologies that organizes and classifies surgical robots used for endoscopy, laparoscopy and thoracoscopy. METHODS: We compiled a list of surgical robots intended to be used for endoscopy, laparoscopy, and/or thoracoscopy by searching United States, European, Hong Kong, Japan, and Korean databases for approved devices. Devices showcased at the 2023 Annual Meeting for the Society of Robotic Surgery were added. We also systematically reviewed the literature for any existing surgical robotic classifications or categorizations. We then created a multidisciplinary committee of 8 surgeons and 2 engineers to construct a proposed classification of the devices included in our search. RESULTS: We identified 40 robotic surgery systems intended to be used for endoscopy, laparoscopy and/or thoracoscopy. The proposed classification organizes robotic devices with regard to architecture, port design, and configuration (modular carts, multi-arm integrated cart, table-attachable or arm-table integration). CONCLUSION: This 3-level classification of robotic surgical devices used for endoscopy, laparoscopy and/or thoracoscopy describes important characteristics of robotic devices systematically.

Purpose: To determine the accuracy of dental implant placement using haptic robotic guidance in a large clinical series. Materials and Methods: In a prospective single-arm clinical study, 108 patients received 273 individual endosteal implants. A virtual preoperative restorative and surgical plan was created from a CBCT scan and matched to the surgical workspace on the day of surgery via either a tooth-based or bone-based fiducial splint. Intraoperatively, the surgeon manipulated a handpiece attached to a haptic robotic guidance arm. A variety of drills and implants were used. Both the osteotomy and the implant placement were guided by 3D haptic constraints according to the virtual plan. Postoperative CBCT scans permitted the calculation of the actual implant placement deviations compared to the plan for accuracy. Precision was calculated by comparing SDs from published literature. Results: The implants were evenly distributed by arch, with 47% placed in the maxilla and 53% in the mandible. The mean ± SD signed depth deviation was 0.14 ± 0.87 mm proud. The global angular deviation averaged 1.42 ± 1.53 degrees, with 95% confidence limits of 1.24 and 1.60 degrees. The crown of the actual placed implant showed an average deviation from the plan of 1.10 ± 0.69 mm and the apex of the placed implant showed a deviation of 1.12 ± 0.69 mm. Haptic robotic guidance showed greater precision than freehand, static computer-guided, and dynamic computer-guided implant placement. Conclusions: This large clinical series of 273 implants shows a high accuracy of implant placement compared to the published accuracy for angular deviations for any technology, as well as demonstrating statistically greater precision. Long-term clinical studies are necessary to establish the true effect of increased accuracy on clinical outcomes. Using haptic robotic guidance provides accurate implant placement while allowing additional benefits compared to computer-guided surgery, namely full visualization of the surgical field and the ability to change the plan intraoperatively. [ABSTRACT FROM AUTHOR]

Background: Aesthetic medicine has traditionally relied on clinical scales for the objective assessment of baseline appearance and treatment outcomes. However, the scales focus on limited aesthetic areas mostly and subjective interpretation inherent in these scales can lead to variability, which undermines standardization efforts. Objective: The consensus meeting aimed to establish guidelines for AI application in aesthetic medicine. Materials and Methods: In February 2024, the AI Consensus Group, comprising international experts in various specialties, convened to deliberate on AI in aesthetic medicine. The methodology included a pre‐consensus survey and an iterative consensus process during the meeting. Results: AI's implementation in Aesthetic Medicine has achieved full consensus for enhancing patient assessment and consultation, ensuring standardized care. AI's role in preventing overcorrection is recognized, alongside the need for validated objective facial assessments. Emphasis is placed on comprehensive facial aesthetic evaluations using indices such as the Facial Aesthetic Index (FAI), Facial Youth Index (FYI), and Skin Quality Index (SQI). These evaluations are to be gender‐specific and exclude makeup‐covered skin at baseline. Age and gender, as well as patients' ancestral roots, are to be considered integral to the AI assessment process, underlining the move towards personalized, precise treatments. Conclusion: The consensus meeting established that AI will significantly improve aesthetic medicine by standardizing patient assessments and consultations, with a strong endorsement for preventing overcorrection and advocating for validated, objective facial assessments. Utilizing indices such as the FAI, FYI, and SQI allows for gender‐specific, age adjusted evaluations and insists on a makeup‐free baseline for accuracy. [ABSTRACT FROM AUTHOR]

This overview examines the latest developments and applications of 3D and 4D printing in the health care and pharmaceutical field. The technique of creating a 3D object from a 3D model in the required size and shape is known as 3D printing. Whereas, 4D printing is the establishment of intricate three-dimensional formations that can change form in response to various external inputs. Applying 3D printing technology 3D printing, the healthcare industry has produced significant strides toward a patient-centred approach. The future of biomedical science and patient-centered care could be completely transformed by 3D printing with further advancements in research and development. 3D, 4D Technology used in printing is one of the most advanced industrial technologies available worldwide. The 3D and 4D printing pharmaceutical enterprises have completed the shift from centralised to distributed systems for the purpose of creating dosage forms. The goal of the study is to support the research goal of determining the degree to which patient-specific treatment is enhanced and healthcare outcomes are improved through the use of printing technologies in pharmaceuticals. Beyond this thorough analysis, the study highlights potential and problems from several angles and comparative aspects between 3D and 4D printing. [ABSTRACT FROM AUTHOR]

Respiratory diseases (RD) are a major healthcare issue and is predicted to be leading cause of mortality by 2030. Artificial intelligence (AI) has been recently gained interest -in scientific fields. Among a variety of application, it has made a marked embrace in medical domain where it is applied for diagnosis and disease progression by clinicians. Further, in particular, AI based Machine learning (ML) and Deep Learning (DL) algorithms have come up as an effective emerging trend in diagnosis of respiratory diseases. These algorithms are trained to classify the respiratory diseases such as pneumonia, fibrosis, cancer, tuberculosis emphysema, asthma based on radiographs, CT scans etc. images. The AI enabled diagnosis can facilitate precise diagnosis and differentiation among overlapping characteristics bearing lung diseases. This review focuses on the AI-based algorithms assisted, improved diagnosis of three respiratory diseases specifically COPD (Chronic obstructive pulmonary disease), asthma and lung fibrosis. Further, AI is expected to play a crucial role in facilitating diagnosis aiding clinicians in predicting and management of lung diseases taking it towards a promising tool for everyday clinical practice soon. It is written with a hope that this brief review of emphasizing utilization of AI in medical field will be helpful to clinicians. [ABSTRACT FROM AUTHOR]

The increasingly aging workforce is bringing particular attention to senior individuals in production sectors. While the interest in Virtual Reality (VR) applications for industrial robotics grows, the question of whether and how senior workers can withstand VR-based repetitive tasks arises. We here aimed to answer such questions by systematically assessing young and senior users' experiential, behavioural, and cognitive factors during simulated robotic teleoperations in VR. Two control systems for VR telerobotics, button- and action-based controls, were employed. Human performance, vigilance, and workload were measured through self-reports and a VR-integrated eye-tracker. Additionally, age-dependent differences in individual cultural and experiential factors were explored via self-report measures. Despite being slower and experiencing increased fatigue under specific conditions, as suggested by the eye-tracking measures, senior users demonstrated comparable precision in operating the robotic arm to their younger counterparts. Notably, both age groups reported similar levels of perceived fatigue. The paper provides an in-depth analysis of the advantages and challenges of adopting advanced telerobotics control systems across different age groups, consistently emphasising the human-centered dimension. [ABSTRACT FROM AUTHOR]

To assess the potential number of European Union (EU) population(s), intervention, comparator(s), and outcomes (PICOs) based on European Network for Health Technology Assessment 21 (EUnetHTA 21) guidance and to explore further evidence-based opportunities to produce more predictable and workable EU PICOs. The consolidated EU PICOs of 2 future hypothetical medicines in first-line non-small cell lung cancer (1L NSCLC) and third line multiple myeloma (3L MM) were derived using published health technology assessment reports of 2 recent medicines in similar indications based on EUnetHTA 21 proposed guidance. Sensitivity analysis assessed the impact of additional PICO requests. The number of analyses requested was estimated. In 1L NSCLC and 3L MM, 6 and 9 EU Member States (MS), respectively, had published health technology assessment reports. PICO consolidation resulted in 10 PICOs for 1L NSCLC and 16 PICOs for 3L MM, increasing to 14 and 18 PICOs, respectively, when England's National Institute for Health and Care Excellence scope was included to proxy remaining MS. A minimum of 280 and 720 analyses would be requested, exponentially increasing as additional outcome measures and subgroups are requested. The PICO approach outlined by EUnetHTA 21 results in a significant number of analysis requests and substantial resources. Use of complementary analyses alongside evidence-based methods to derive PICOs and engaging with the health technology developer throughout the process would create a workable EU PICO that is predictable and most impactful for the EU, resulting in a timely and high-quality assessment report that is more usable at a MS level. • The European Union Health Technology Assessment Regulation will provide a framework for joint clinical assessment for new medicines, starting with oncology and advanced therapy medicinal products. It calls for the need for a harmonized transparent scoping criteria, following a scientific evidence-based process and based on state-of-the-art medical science. • We followed European Network for Health Technology Assessment 21 Consortium guidance to determine the number of population(s), intervention, comparator(s), and outcomes (PICOs) that would be required for 2 hypothetical products in 2 common oncology indications. We found that significantly more PICOs than anticipated would be required, most of which would require indirect treatment comparisons, without further consolidation guidance. The result would have greater analytical complexity and resources, compromising workability within the time frame of the assessment. • Further consolidation of the PICO process for joint clinical assessment in a transparent and predictable manner following evidence-based medicine principles, with health technology developer engagement, is key to ensuring that the Health Technology Assessment Regulation can facilitate harmonization of health technology assessment across Europe and ultimately improve patient access to new medicines. [ABSTRACT FROM AUTHOR]

Context.--Workup of the poorly differentiated or undifferentiated tumor remains a significant and challenging entity in the practice of anatomic pathology. Particularly in the setting of small biopsies and limited material, these cases demand a balanced approach that considers the patient's clinical and radiologic presentation, a basic assessment of tumor morphology, a reasonably broad immunohistochemical panel, and diligent preservation of tissue for prognostic and therapeutic studies. Objective.--To illustrate some of the new and emerging immunohistochemical markers in the evaluation of tumors with undifferentiated or poorly differentiated morphology, with a focus on the workup in limited tissue samples to raise awareness of the issues involved with the pathologic workup in these challenging tumors. Data Sources.--A literature review of new ancillary studies that can be applied to cytologic specimens was performed. Conclusions.--Knowledge of the patient's history and communication with the patient's clinical team is essential in formulating a differential diagnosis that can appropriately limit the differential diagnosis based on morphology, especially in small specimens. This information, in conjunction with classifying the tumor morphology (eg, epithelioid, spindled, neuroendocrine, basaloid/biphasic, mixed) gives a logical approach to choosing an initial immunohistochemical panel. Fortunately, immunohistochemistry is evolving quickly in the wake of groundbreaking molecular studies to develop new and better markers to further classify these difficult tumors beyond where we traditionally have been able to go. [ABSTRACT FROM AUTHOR]

This article examines the value of medical technology through the case of electroencephalograms (EEGs), devices used to visualize brain activity and diagnose seizures. Drawing on ethnographic fieldwork, the article shows that EEGs are valued differently by patients and medical practitioners. While practitioners value EEGs for their clinical utility, i.e., ability to inform clinical decisions, patients value EEGs even in the absence of clinical utility. Indeed, patients derive long-lasting therapeutic effects from this diagnostic technology. These findings intervene in the utilitarian calculus of therapeutic value—a mode of reasoning that equates value with clinical utility—commonly deployed in biomedicine and engineering and call for a recognition of alternative notions such as the therapeutic value of being witnessed and cared for by medical experts via EEGs and other technologies that require time to work. Expansive notions of therapeutic value are imperative for including marginalized patients—especially low-income, disabled, and women patients—in debates on automation and the future of healthcare. Studying how multiple stakeholders value a medical technology provides insight into valuation, objectification, expertise, and other concerns central to science and technology studies. [ABSTRACT FROM AUTHOR]

The author reflects on why health communicators and researchers should prioritize documenting the impact of the work of their peers as the field continues to grow and evolve. He discusses trends and observations in the essays documenting impact, benefits and challenges of documenting impact for health communicators, and the impact of Dr. Teresa Thompson, who most of the field knows simply as Teri, on health communication research.

The Health Communication and Informatics Research Branch at the U.S. National Cancer Institute was founded in 1999 in response to increasing evidence demonstrating a link between effective health communication and improved cancer-related outcomes and in recognition of the rapid and dramatic technological changes that were transforming health communication at the turn of the 21st century. For the past 25 years, the Health Communication and Informatics Research Branch has been conducting and supporting research at the forefront of emerging cancer communication trends and technologies, making numerous contributions to health communication science, public health, and cancer control practice. In this essay, we provide a brief history of the branch and the context that led to its establishment, discuss contributions made by the branch to health communication research and practice through key projects and initiatives, and conclude by highlighting health communication and informatics research priorities that offer opportunities for significant impact going forward in light of the challenges posed by the current communication environment. [ABSTRACT FROM AUTHOR]

Background and Objective: Accurately extrapolating survival beyond trial follow-up is essential in a health technology assessment where model choice often substantially impacts estimates of clinical and cost effectiveness. Evidence suggests standard parametric models often provide poor fits to long-term data from immuno-oncology trials. Palmer et al. developed an algorithm to aid the selection of more flexible survival models for these interventions. We assess the usability of the algorithm, identify areas for improvement and evaluate whether it effectively identifies models capable of accurate extrapolation. Methods: We applied the Palmer algorithm to the CheckMate-649 trial, which investigated nivolumab plus chemotherapy versus chemotherapy alone in patients with gastroesophageal adenocarcinoma. We evaluated the algorithm's performance by comparing survival estimates from identified models using the 12-month data cut to survival observed in the 48-month data cut. Results: The Palmer algorithm offers a systematic procedure for model selection, encouraging detailed analyses and ensuring that crucial stages in the selection process are not overlooked. In our study, a range of models were identified as potentially appropriate for extrapolating survival, but only flexible parametric non-mixture cure models provided extrapolations that were plausible and accurately predicted subsequently observed survival. The algorithm could be improved with minor additions around the specification of hazard plots and setting out plausibility criteria. Conclusions: The Palmer algorithm provides a systematic framework for identifying suitable survival models, and for defining plausibility criteria for extrapolation validity. Using the algorithm ensures that model selection is based on explicit justification and evidence, which could reduce discordance in health technology appraisals. [ABSTRACT FROM AUTHOR]

Survival extrapolation often plays an important role in health technology assessment (HTA), and there are a range of different approaches available. Approaches that can leverage external evidence (i.e. data or information collected outside the main data source of interest) may be helpful, given the extent of uncertainty often present when determining a suitable survival extrapolation. One of these methods is the multi-parameter evidence synthesis (MPES) approach, first proposed for use in HTA by Guyot et al., and more recently by Jackson. While MPES has potential benefits over conventional extrapolation approaches (such as simple or flexible parametric models), it is more computationally complex and requires use of specialist software. This tutorial presents an introduction to MPES for HTA, alongside a user-friendly, publicly available operationalisation of Guyot's original MPES that can be executed using the statistical software package R. Through two case studies, both Guyot's and Jackson's MPES approaches are explored, along with sensitivity analyses relevant to HTA. Finally, the discussion section of the tutorial details important considerations for analysts considering use of an MPES approach, along with potential further developments. MPES has not been used often in HTA, and so there are limited examples of how it has been used and perceived. However, this tutorial may aid future research efforts exploring the use of MPES further. [ABSTRACT FROM AUTHOR]

Background: The National Institute for Health and Care Excellence (NICE) may approve health technologies on condition of more evidence generated only in research (OiR) or only with research (OwR). NICE specifies the information needed to comply with its request, although it may not necessarily guarantee good quality and timely evidence for re-appraisal, before reaching a final decision. Aim: This study aimed to critically appraise the methods, quality and risk of bias of evidence generated in response to NICE OiR and OwR technology appraisal (TA) and highly specialised technologies (HSTs) recommendations. Methods: NICE TAs (between March 2000 and September 2020) and HST evaluations (to October 2023) of medicines were reviewed. Conditional recommendations were analysed to identify the evidence requested by NICE for re-appraisal. The new evidence was analysed for compliance with NICE's request and assessed using the Cochrane Collaboration's tools for risk of bias in randomised trials and the ROBINS-I tool for non-randomised evidence. Results: NICE made 54 conditional recommendations from TAs (13 OiR and 41 OwR) and five conditional recommendations for HSTs (all OwR). Of these, 16 TAs presented additional evidence for re-appraisal (9 OiR [69%] and 7 OwR [17%]) and three HSTs (3 OwR [60%]). Two of the nine re-appraised TAs with OiR recommendation and four of the seven OwR complied fully with NICE's request for further evidence, while all three from the HSTs complied. The majority of re-appraised TAs and HSTs included evidence that was deemed to be at serious, high, moderate or unclear risk of bias. Among the 26 randomised controlled trials from TAs assessed, eight were categorised as having low risk of bias in all domains and ten had at least one domain as a high risk of bias. Reporting was unclear for the remainder. Twenty-two non-randomised studies, primarily single-arm studies, were susceptible to biases mostly due to the selection of participants and to confounding. Two HSTs provided evidence from randomised controlled trials which were classified as unclear or high risk of bias. All non-randomised evidence from HSTs were categorised as moderate or serious risk of bias. Conclusions: There is widespread non-compliance with agreed data requests and important variation in the quality of evidence submitted in response to NICE conditional approval recommendations. Quality standards ought to be stipulated in respect to evidence contributing to re-appraisals following NICE conditional approval recommendations. [ABSTRACT FROM AUTHOR]

This article explores the ways in which Australian health professionals performatively constitute the relationships between performance and image-enhancing drugs (PIEDs), health, and masculinity. It draws on the work of Judith Butler, John Law and Bruno Latour to argue that interviews with health professionals stabilize PIEDs, somewhat paradoxically, as inherently dangerous substances that distort and damage "natural" bodies, and as important medical technologies that can reverse this damage when administered "legitimately." On the basis of interviews with 20 Australian health professionals who, in the course of their work, encounter men who consume PIEDs, we identify some of the mechanisms through which PIED consumers are excluded from health services, and are sometimes enacted as difficult and deceitful. Rather than taking these as reflecting pre-existing truths about PIED consumers, we show how, when framed within the assumed authority of medical expertise, discourses of "the natural," health and masculinity produce these realities. Through these entanglements, masculinity is performed as natural to the body, and self-guided, experimental and illicit consumption of drugs constitutes the consumer, his body and his masculinity as unnatural, deceptive, and inauthentic. We conclude by arguing that treating men who consume PIEDs as potential co-experts, acknowledging the uses of PIEDs, and having open discussions could improve health outcomes for this group. Such a move, we propose, might also intervene in the iterative practices that work to regulate so-called "real" and "authentic" masculinities to create more expansive possibilities for men. [ABSTRACT FROM AUTHOR]

PURPOSE: Precision oncology relies on accurate and interpretable reporting of testing and mutation rates. Focusing on the BRAFV600 mutations in advanced colorectal carcinoma, non–small-cell lung carcinoma, and cutaneous melanoma, we developed a platform displaying testing and mutation rates reported in the literature, which we annotated using an artificial intelligence (AI) and natural language processing (NLP) pipeline. METHODS: Using AI, we identified publications that likely reported a testing or mutation rate, filtered publications for cancer type, and identified sentences that likely reported rates. Rates and covariates were subsequently manually curated by three experts. The AI performance was evaluated using precision and recall metrics. We used an interactive platform to explore and present the annotated testing and mutation rates by certain study characteristics. RESULTS: The interactive dashboard, accessible at the BRAF dimensions website, enables users to filter mutation and testing rates with relevant options (eg, country of study, study type, mutation type) and to visualize annotated rates. The AI pipeline demonstrated excellent filtering performance (>90% precision and recall for all target cancer types) and moderate performance for sentence classification (53%-99% precision; ≥75% recall). The manual annotation of testing and mutation rates revealed inter-rater disagreement (testing rate, 19%; mutation rate, 70%), indicating unclear or nonstandard reporting of rates in some publications. CONCLUSION: Our AI-driven NLP pipeline demonstrated the potential for annotating biomarker testing and mutation rates. The difficulties we encountered highlight the need for more advanced AI-powered literature searching and data extraction, and more consistent reporting of testing rates. These improvements would reduce the risk of misinterpretation or misunderstanding of testing and mutation rates by AI-based technologies and the health care community, with beneficial impacts on clinical decision-making, research, and trial design. INSIDE BRAF dashboard uses AI to show cancer biomarker testing/mutation rates and aid clinicians in decision making. [ABSTRACT FROM AUTHOR]

PURPOSE: There is an increasing interest in studying the potential of mobile health (mHealth) technologies, such as smartphone apps and wearables, as monitoring tools for patients with cancer during or after their treatment. However, little research is dedicated to exploring the opinions and concerns of patients regarding the adoption of these technologies. This study aimed to gain insight into patients' perspectives and preferences for participating in mHealth-based monitoring in cancer care. METHODS: A qualitative study comprising semistructured interviews was conducted in the Netherlands between April and June 2023. Participants were eligible if they were 18 years or older with a current or past diagnosis of cancer. The interview guide was developed on the basis of the technology acceptance model, with main themes being use, communication, trust, privacy, and expectations. RESULTS: Thirteen participants with urologic primary cancer were interviewed. Most patients had already some familiarity with the use of digital monitoring devices or wearables. Main barriers included persistent reminders of the illness, receiving notifications deemed unnecessary or unwanted, and the acknowledgment that mHealth technology does not serve as a substitute for human doctors. Conversely, patients recognized the potential for time-savings through the utilization of mHealth, viewed active monitoring as nonburdensome, considered mHealth a tool for reducing the communication threshold with their doctor, and expressed willingness to adopt such a platform if they perceived personal or societal relevance. CONCLUSION: This study has elucidated which factors are important for successful development of mHealth for patients with cancer. While both barriers and facilitators play a role, patients' attitudes were positive toward the implementation of remote digital monitoring, showing promising prospects for future research of mHealth in oncology. [ABSTRACT FROM AUTHOR]

Background: Neurobehavioural comorbidities have a detrimental effect on the quality of life of people with epilepsy, yet tracking their impact is challenging as behaviour may vary with seizures and anti‐seizure medication (ASM) side effects. Smartphones have the potential to monitor day‐to‐day neurobehavioural patterns objectively. We present the case of a man in his late twenties with drug‐resistant focal epilepsy in whom we ascertained the effects of ASM withdrawal and a convulsive seizure on his touchscreen interactions. Methods: Using a dedicated app, we recorded over 185 days the timestamps of 718,357 interactions. We divided the various smartphone behaviours according to the next‐interval dynamics of the interactions by using a joint interval distribution (JID). During two ASM load transitions, namely before versus during tapering and tapering versus restarting medication, we used cluster‐based permutation tests to compare the JIDs. We also compared the JID of the seizure day to the average of the previous 3 days. Results: The cluster‐based permutation tests revealed significant differences, with accelerated next‐interval dynamics during tapering and a reversal upon medication restart. The day of the convulsion exhibited a marked slowing of next‐interval dynamics compared to the preceding 3 days. Conclusion: Our findings suggest that the temporal dynamics of smartphone touchscreen interactions may help monitor neurobehavioural comorbidities in neurological care. [ABSTRACT FROM AUTHOR]

Background: Hypertension is an important cardiovascular risk factor with potentially harmful consequences. Home blood pressure monitoring is a promising method for following the effect of hypertension treatment. The use of technology-enabled care and increased patient involvement might contribute to more effective treatment methods. However, more knowledge is needed to explain the motivations and consequences of patients engaging in what has been called 'do-it-yourself healthcare'. Aim: This study aimed to investigate patients' experiences of home blood pressure monitoring through the theoretical frame of the Unified Theory of Acceptance and Use of Technology (UTAUT 2). Methods: The study had a qualitative design, with focus group interviews using the web-based platform Zoom. The data were analysed using qualitative deductive content analysis, inspired by Graneheim and Lundman. Results: The results are presented using the seven theoretical constructs of UTAUT 2: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonistic Motivation, Price Value and Habit. We found one overarching theme ‒ 'It's all about the feeling of security'. The patients were influenced by relatives or healthcare personnel and experienced the home monitoring process as being easy to conduct. The patients emphasised that the quality of the blood pressure monitor was more important than the price. Patients reported home monitoring of blood pressure as a feasible method to follow-up care of their hypertension. Discussion: This study indicates that among motivated patients, home blood pressure measurement entails minimal effort, increases security, and leads to better communication about blood pressure between healthcare personnel and patients. KEY POINTS: Self-monitoring of hypertension is an increasingly common method and may increase measurement accuracy and patient involvement. Through the theoretical lens of the UTAUT2, home blood pressure monitoring seems to increase patients´ feeling of security. The respondents did not report negative experiences and might have been more prone to use technology-enabled care. Home blood pressure monitoring seems to be easily adopted by motivated patients with an interest in self-monitoring their disease. [ABSTRACT FROM AUTHOR]

This study examines people's acceptance of the fifth generation (5G) of wireless and mobile communication technologies at a time when such services and devices were just approaching consumer availability. It utilizes the Technological Acceptance Model to the frame analysis of key perceptions of 5G technology, as well as the social climate surrounding it. Quantitative and qualitative data were gathered from adult samples in China and the United States to assess how they understand and perceive 5G technology. The results show that perceived technological features (e.g., perceptions of usefulness and speed) explain the favorable attitudes and adoption intentions in the two countries, whereas personal privacy concerns explain the negative attitudes and intentions. The results also demonstrate that, while 5G technology was often regarded as an instrument for national elevation in China, we see more concerns about rumored negative effects of the technology on health in the United States. [ABSTRACT FROM AUTHOR]

Background and Aim: Chatbots are computer programs, which are devised to simulate conversations through voice or textual interactive forms. These applications offer multiple benefits in patient education, clinical decision-making, interpersonal communication, research activities, data analysis and administrative affairs. The present scoping review aims to analyse the current role, pitfalls, challenges and future scope of these modalities in diverse fields of medical science. Methods: Literature search was made on 9 April 2024, on five databases (PubMed, Scopus, Web of Science, Embase and Google Scholar). A narrative approach was used for the synthesis of results. Results: Our literature search yielded 1024 studies. After the de-duplication of manuscripts using Endnote, 342 articles were identified. After title and abstract screening, 74 articles were included for the next round of screening. Finally, 14 articles were selected for this scoping review.Diverse chatbot applications have been developed at a growing rate for use in medical science. There is a gradual shift towards employing machine learning-based strategies to develop chatbot programs. Chatbots have a significant potential to revolutionise diverse aspects of medicine including patient care, academic activities, and research endeavours. Conclusion: Artificial Intelligence can be highly effective in streamlining the routine functions and activities in medical care. Chatbots have the potential to meliorate the accessibility, efficiency and standard of medical care. However, the need for further validation through high-quality studies to ensure patient privacy, strategies to conform to patients' data security, accuracy and precision of the technology and mitigating potential pitfalls in its routine utilisation globally are warranted. [ABSTRACT FROM AUTHOR]

This article reviews the evolution of noninvasive hemodynamic monitoring technologies, highlighting their importance in perioperative and critical care settings. Initially dominated by invasive methods, the field has shifted toward noninvasive techniques to reduce risks and improve patient safety. These advancements encompass various technologies, including bioimpedance/bioreactance, pulse contour analysis, and photoplethysmography, offering anesthesiologists dynamic tools for patient management. The article explores historical developments, traditional and advanced noninvasive monitors, and future trends, emphasizing the potential of integrating artificial intelligence and wearable technology in patient care. [ABSTRACT FROM AUTHOR]

In 2020, the World Health Organization (WHO) estimated that 2.3 million women worldwide were diagnosed with breast cancer, which resulted in 685,000 deaths. According to the projections, the number of women who have been diagnosed with breast cancer over the last five years before and by the end of 2020 was expected to reach 7.8 million, making it the most common type of cancer worldwide. Early diagnosis could prevent the ailment, however, lack of availability of health facilities and the cost of accessing treatment, especially in developing nations, are among the challenges confronting the solution. With the advent of artificial intelligence and machine learning models, specifically Convolutional Neural Networks (CNNs), considering their multiple architectures is highly promising to address the challenge of early diagnosis. Therefore, this study aims to propose an architecture of CNNs that gives the best accuracy, F1 score, and Cohen Kappa score among the Custom Optimized CNN, ResNet, and EfficientNet architectures. From the results, ResNet's performance across the five metrics outweighs the other two architectures. While ResNet reported an accuracy, precision, and F1 score of 0.9987, 0.9934, and 0.9950, respectively, EfficientNet, which has the second performance, reported 0.9977, 0.9914, and 0.9939 as accuracy, precision, and F1 score, respectively. Therefore, the best-performing architecture can be deployed for other available breast cancer datasets in order to ensure its total efficiency. [ABSTRACT FROM AUTHOR]

Background: Cost-utility analysis typically relies on preference-based measures (PBMs). While generic PBMs are widely used, disease-specific PBMs can capture aspects relevant for certain patient populations. Here the EORTC QLU-C10D, a cancer-specific PBM based on the QLQ-C30, is validated using Dutch trial data with the EQ-5D-3L as a generic comparator measure. Methods: We retrospectively analysed data from four Dutch randomised controlled trials (RCTs) comprising the EORTC QLQ-C30 and the EQ-5D-3L. Respective Dutch value sets were applied. Correlations between the instruments were calculated for domains and index scores. Bland–Altman plots and intra-class correlations (ICC) displayed agreement between the measures. Independent and paired t-tests, effect sizes and relative validity indices were used to determine the instruments' performance in detecting clinically known-group differences and health changes over time. Results: We analysed data from 602 cancer patients from four different trials. In overall, the EORTC QLU-C10D showed good relative validity with the EQ-5D-3L as a comparator (correlations of index scores r = 0.53–0.75, ICCs 0.686–0.808, conceptually similar domains showed higher correlations than dissimilar domains). Most importantly, it detected 63% of expected clinical group differences and 50% of changes over time in patients undergoing treatment. Both instruments showed poor performance in survivors. Detection rate and measurement efficiency were clearly higher for the QLU-C10D than for the EQ-5D-3L. Conclusions: The Dutch EORTC QLU-C10D showed good comparative validity in patients undergoing treatment. Our results underline the benefit that can be achieved by using a cancer-specific PBM for generating health utilities for cancer patients from a measurement perspective. [ABSTRACT FROM AUTHOR]

Head and neck reconstruction poses challenges requiring stable coverage and optimal appearance and functionality. In complex cases, and beyond the right choice of flap, Virtual Reality (VR) surgical software and preoperative planning, are needed to optimize outcomes. Our patient is a 16-year-old with a history of rhabdomyosarcoma necessitating maxillary-mandibular defect reconstruction. VR-based preoperative planning facilitated the intervention by creating 3D-VR models of facial anatomy and fibula, simulating resection margins, and creating surgical guides for intraoperative use. Our 2-team surgeons opted for a single-staged reconstruction using a vascularized fibula free flap. This case demonstrates successful single-flap reconstruction of a maxillary-mandibular defect, addressing challenges of recipient vessel scarcity, fibrotic scarring, and poor tissue quality. It also underscores the efficacy of a 2-team approach and VR-based preoperative planning in achieving successful outcomes in complex maxillary-mandibular reconstruction. The use of a single fibula flap minimized possible complications and operative time, and the 2-team approach helped by combining the expertise and training of craniofacial and microsurgical trained surgeons. Acknowledging limitations in imaging quality and real-time adaptability, we advocate for continued exploration of VR technologies in enhancing surgical precision in challenging cases. [ABSTRACT FROM AUTHOR]

In mobile technologies for health and well-being (mHealth), push notifications are a widely used tool to implement persuasive strategies. However, little research has been carried out to investigate the impact of notifications in such technologies and users' attitudes towards them. In this study, we address this gap by exploring the role of notifications and their customisation in the context of mobile applications that promote social, mental, or physical well-being. Based on observational data collected from 152 participants, we investigate users' attitudes and preferences towards notifications and explore how they differ along demographic dimensions. In addition, we then verified these results with behavioural measures and attitude ratings through a user study (N = 31), which saw participants interacting with a prototype of a mHealth application. Our findings show that customisation of notifications is a desired feature that can be used to improve the persuasive aspect of mHealth applications. Implications derived from our study can provide guidance for researchers and practitioners alike when designing app notifications to create better motivating, meaningful, and persuasive experiences. [ABSTRACT FROM AUTHOR]

The effectiveness of using a balloon guide catheter during endovascular thrombectomy in patients with acute ischaemic stroke due to large vessel occlusion of the anterior circulation remains uncertain. We aimed to assess the effectiveness and safety of using a balloon guide catheter during endovascular thrombectomy, compared with using a conventional guide catheter, in this patient population. We conducted a multicentre, open-label, blinded-endpoint, randomised controlled trial at 28 hospitals in China. Adults aged 18 years or older with acute ischaemic stroke who were able to receive endovascular thrombectomy within 24 h after symptom onset, according to local guidelines, were eligible. Patients were randomly assigned (1:1) to the balloon guide catheter group or the conventional guide catheter group using a central internet-based system with a minimisation algorithm. The treating physicians and patients were aware of treatment allocation, but clinical outcomes were collected by local trained physicians who were masked. Neurological assessments were performed at baseline, 24 h after randomisation, and at 7 days or hospital discharge (whichever occurred first). The primary outcome was functional recovery, assessed by change in modified Rankin Scale (mRS) scores (ranging from 0 [no symptoms] to 6 [death]) at 90 days in the intention-to-treat population and obtained from structured interviews. Treatment effect was estimated using ordinal logistic regression with adjustment for site and baseline prognostic factors (time from symptom onset to randomisation, the preferred thrombectomy strategy, baseline National Institutes of Health Stroke Scale score, prestroke function [estimated mRS score], and age). This trial is registered with ClinicalTrials.gov , NCT05592054 (terminated). Between Feb 7 and Nov 13, 2023, 1698 patients were assessed for eligibility and 329 were randomly assigned to the balloon guide catheter group (n=164) or conventional guide catheter group (n=165) when the trial was paused and subsequently terminated on April 18, 2024, due to safety concerns. The median age was 69 years (IQR 59–76). Of 329 patients, 201 (61%) were male and 128 (39%) were female. Participants in the balloon guide catheter group had significantly worse scores on the mRS at 90 days than those in the conventional guide catheter group (adjusted common odds ratio 0·66 [95% CI 0·45–0·98]; p=0·037). All-cause mortality at 90 days was numerically higher in the balloon guide catheter group than in the conventional guide catheter group (39 [24%] vs 26 [16%]). There were no statistically significant differences between groups in intracranial haemorrhage, symptomatic intracranial haemorrhage, or other serious adverse events. Compared with conventional guide catheters, the use of balloon guide catheters led to worse functional recovery in patients receiving endovascular thrombectomy for intracranial large vessel occlusion. Future studies are needed to confirm these results. National Natural Science Foundation of China, Shanghai Hospital Development Center, Biopharma Industry Promotion Center Shanghai, and Ton-bridge Medical Technology. For the Chinese translation of the abstract see Supplementary Materials section. [ABSTRACT FROM AUTHOR]

Background: Pulse oximeters are vital for assessing blood oxygen levels but can produce inaccurate readings during patient motion, leading to false alarms and alarm fatigue. Analog SpO2 Technology, which uses analog waveforms to filter motion artifacts, may improve accuracy compared to digital sensors. However, the effectiveness of this technology in reducing false alarms in clinical settings remains unclear. This study assesses and compares the motion resistance of Analog SpO2 Technology of two devices in the market. Methods: Thirty healthy adults underwent controlled experiments (Control, Linear Motion, Angular Motion) using two pulse oximeters. Linear Motion tested hand displacement impact, while Angular Motion involved rhythmic hand motions at 120 bpm and 160 bpm. Results: Both devices performed similarly in Control, with no disruptions. In Linear Motion, mild disruptions occurred, but no significant differences in SpO2 readings or alarms. Angular Motion at 120 bpm showed stability with no alarms. At 160 bpm, Device B (Biolight Analog SpO2) had fewer technical alarms but more SpO2 alarms than Device A (Masimo Analog SpO2). Conclusions: Analog SpO2 exhibited motion resistance under static, linear and continuous waving angular motion up to 120 bpm and 160 bpm, but alarms occurred at 160 bpm with continuous tapping angular motion. These findings signify non-inferiority of either device in clinical settings. Further studies should include patients with cardiovascular and/or respiratory diseases. Trial Registration: The study was submitted to and approved by the Biolight Ethics Committee (S0723), and written informed consent from all participants was obtained. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality globally, with increasing rates in low- and low-middle-income countries (LMICs), particularly sub-Saharan Africa (SSA). These occur due to exposure to various risk factors, such as tobacco smoking, physical inactivity, hypertension, and diabetes. There are unique features in LMICs that contribute to the risk factor profile and quality of care offered in these countries. These include cultural influences on diet (carbohydrate-dense diet, use of salt-based spices and preservatives), the lack of dedicated spaces for physical activity, and perceptions of desirable body sizes. The attendant complications affect individuals, families and the community at large. Furthermore, there is a dearth of research to inform change at both the individual and policy level in LMICs. Lifestyle modification is the cornerstone of risk factor control and requires engaging relevant stakeholders in healthcare to enforce sustainable behaviour change among individuals. This includes measures from the individual level to national leadership and requires multisectoral collaboration to ensure sustainability. Such methods include building health worker capacity through a task shifting approach, strengthening the health worker-patient relationship, employing digital/technological applications, and engaging families and communities to serve as accountability partners in the management of CVD and related risk factors. There is a need for increased contextually relevant research output to inform contemporary methods to improve CVD and risk factor control in LMICs. [ABSTRACT FROM AUTHOR]

Background: Cost-effectiveness analyses of health technologies have become a part of the decision-making process in healthcare policies. Nevertheless, economic results are not always presented in comprehensible formats for non-technical audiences, such as the general population, healthcare professionals or decision-makers. The purpose of this study was to observe which formats best convey the key message of an economic evaluation, and which are best received by two different audiences. Methods: The summary of a hypothetical cost-effectiveness analysis was edited in different formats: infographic, plain language text and video-abstract for the general population; executive summary and policy brief for a specialized population, i.e. clinicians, clinical and non-clinical managers, or methodologists in health-technology assessment. Participants were randomly shown one of the formats, and data on objective and subjective comprehension, and perceived usefulness/acceptability were gathered by means of online questionnaires. Statistical differences between formats within each audience were analysed. Results: In the general population (N = 324), objective comprehension was statistically significantly better for infographic than for video-abstract (p = 0.005), and for plain text than for video-abstract (p = 0.024). There were no differences in subjective comprehension, but video-abstract was considered statistically significantly more useful to understand the information than plain text (p = 0.011). In the specialized population (N = 100), no statistically significant differences were observed for objective and subjective comprehension, although policy brief was perceived as statistically significantly more useful than executive summary (p = 0.005). Conclusions: A balance between effectivity of conveying the message and attractivity of the format needs to be sought, to facilitate non-technical audiences' understanding of economic data and, consequently, perceive decision-making processes as more transparent and legitimate. The infographic and policy brief could be robust ways to present economic data to the general public and specialized audience, respectively. [ABSTRACT FROM AUTHOR]

Drug development for children presents unique challenges and is highly regulated. Novel approaches, such as the use of extrapolation to address, for example, the need to avoid unethical studies, whilst supporting robust evidence generation have been developed in support of benefit/risk considerations by regulatory authorities. This is only one step in the decision‐making process towards access, which in Europe also includes health technology assessment (HTA) bodies.Discussions related to evidentiary requirements in small populations using scientific evidence transfer have been identified as a priority action by European Medicines Agency/European Network for Health Technology Assessment 21 (EMA/EUnetHTA 21). We describe the outcome of this work and reflect on the discussions that had taken place on how to leverage prior knowledge through identifying and addressing uncertainties during life cycle management to support regulatory and HTA decision‐making. Using examples, we explore the range of applications for evidence generation and offer regulatory and HTA insights on key design considerations for producing better evidence, reflecting our shared ambition. Early interactions with all respective stakeholders, particularly between regulators and HTA bodies are key to optimise data generation and utility in children.In Europe, the HTA regulation will offer opportunities for collaborations, which are important for all development efforts. We collaboratively explored the unique specific challenges relating to paediatric drug development, ethically and in its ability to leverage prior knowledge, as exemplified using extrapolation. Learnings from these offer opportunities to further develop methodology on how to leverage uncertainties across a product's life cycle for small populations generally. [ABSTRACT FROM AUTHOR]

Background: The quality of Big Data analysis in medicine and surgery heavily depends on the methods used for clinical data collection, organization, and storage. The Knowledge Graph (KG) represents knowledge through a semantic model, enhancing connections between diverse and complex information. While it can improve the quality of health data collection, it has limitations that can be addressed by the Decentralized (blockchain-powered) Knowledge Graph (DKG). We report our experience in developing a DKG to organize data and knowledge in the field of emergency surgery. Methods and results: The authors leveraged the cyb.ai protocol, a decentralized protocol within the Cosmos network, to develop the Emergency Surgery DKG. They populated the DKG with relevant information using publications from the World Society of Emergency Surgery (WSES) featured in the World Journal of Emergency Surgery (WJES). The result was the Decentralized Knowledge Graph (DKG) for the WSES-WJES bibliography. Conclusions: Utilizing a DKG enables more effective structuring and organization of medical knowledge. This facilitates a deeper understanding of the interrelationships between various aspects of medicine and surgery, ultimately enhancing the diagnosis and treatment of different diseases. The system's design aims to be inclusive and user-friendly, providing access to high-quality surgical knowledge for healthcare providers worldwide, regardless of their technological capabilities or geographical location. As the DKG evolves, ongoing attention to user feedback, regulatory frameworks, and ethical considerations will be critical to its long-term success and global impact in the surgical field. [ABSTRACT FROM AUTHOR]

There is a national call for digital health innovations in the provision of health and social care, especially for older adults in the context of home. The Reflective STRENGTH-Giving Dialogue (STRENGTH) method is used to provide individual and holistic care using recurrent dialogues. The SelfSTRENGTH application (app) was developed to support reflection and stimulate activity that enables achievement of meaningful life projects. The objective of the study was to describe how the use of the SelfSTRENGTH app is perceived by older adults living with long-term health problems. A qualitative, descriptive and inductive design. Data, analyzed using a phenomenographic approach, consisted of individual qualitative interviews with older adults (age range = 74–96) conducted before (n = 34) and after (n = 27) participation in the STRENGTH intervention. The results consist of five categories of description: Resistance to use apps and digital tools; Health problems hinder its usefulness; Need of assistance is a prerequisite to be able to use the app; the app supports memory and reflection; and Using the app disperses thoughts and creates motivation. The outcome space thus contains descriptions of both barriers and possibilities regarding how the use of the SelfSTRENGTH app is perceived. The potential of using technology such as applications in health care is immense. However, long-term health problems in older adults, along with care providers’ attitudes and opportunities to support, must be taken into consideration when implementing apps in health and social care. [ABSTRACT FROM AUTHOR]

Radiological findings play an essential and complementary role in diagnosing Covid-19, assessing its severity, and managing its patients. Artificial intelligence technology based on medical imaging, which has made exciting developments by being applied in many areas, has become an area of interest for the rapid and accurate detection of the disease in the fight against the Covid-19 pandemic. The main difficulty is the inability to obtain a large dataset size with quality and standard images that neural networks need to perform well. Aiming at this problem, this study proposes a Siamese neural network-based deep learning framework for accurate diagnostics of Covid-19 using chest X-ray (CXR) images. The pre-trained VGG16 architecture, based on the transfer learning approach, forms the backbone of the Siamese neural network. The outputs of the backbones are joined together by a merging layer, and then the output passes through a fully connected layer. Based on this structure, category-aware Siamese-based models are produced for each class. The predictions from the models are combined using a voting mechanism to reduce the possibility of misclassification and to make better decisions. The framework was evaluated using a publicly available dataset for the 4-class classification task for Covid-19 pneumonia, lung opacity, normal, and non-Covid-19 viral pneumonia images. The findings reveal the high discrimination ability of the framework, trained using only 10 images per class in less training time, achieving an average test accuracy of 92%. Our framework, which learns a single Siamese-based pairwise model for each class, effectively captures class-specific features. Additionally, it has the potential to deal with data scarcity and long training time problems in multi-class classification tasks. [ABSTRACT FROM AUTHOR]

Background: With the onset of the COVID-19 pandemic, the world witnessed an unprecedented surge in the adoption of digital platforms across various sectors, including mental health care. In countries such as Pakistan, where traditional face-to-face therapy practices hold social and cultural significance, transitioning to digital therapeutic methods presents a range of unique opportunities and challenges. Objectives: This research aimed to explore the dynamics, implications, and perceptions surrounding digital therapeutic care within the Pakistani sociocultural context. Given the paucity of literature on this subject in the Pakistani context, this study aims to bridge the evidence gap between trends in digital therapy and localized practices. The main goal was to understand the benefits, issues, and challenges therapists and clients face when adopting digital modes for therapeutic care. Method: For this study, primary data was gathered from counsellors and therapists using a qualitative in-depth interview guide. Using a thematic analysis approach, key themes were derived from the interviews that provided insights into the experiences and perceptions of the participants. Results: The study revealed that digital platforms have great potential in breaking down geographical barriers making therapeutic interventions more accessible to a wider demographic. This increased accessibility also brought about a level of comfort for clients as they could access therapy from their familiar surroundings. Among the challenges that needed attention, security and confidentiality stood out, requiring strict measures to safeguard client's data. The shift also brought to light the diverse range of feedback from clients, which was influenced by factors like age and technological proficiency. Moreover, the digital transition posed challenges for both therapists and clients, with many facing an adjustment period as they transitioned from face-to-face to online sessions. One significant challenge was the perceived difficulty in fostering a deep interpersonal connection in a virtual environment. This was further compounded by the need for therapists to modify traditional therapeutic techniques to fit the online mode. Conclusion and implications: The study underscores the evolving nature of digital therapy in Pakistan, setting a foundation for further exploration in aligning technology with therapeutic needs, ensuring optimal benefits for clients while preserving the sanctity and efficacy of the therapeutic relationship. [ABSTRACT FROM AUTHOR]

Background: The interplay between infertility and intimate partner violence (IPV) is a pressing global health issue, yet qualitative evidence on this phenomenon is limited. In order to measure and prevent IPV, it is pivotal to explore and integrate perceptions and experiences from those women with infertility themselves on IPV definition and its phenomenal manifestations in the context of infertility. Methods: This study is undertaken as part of a year-long ethnographic research based at a reproductive medicine center in Beijing, China from July 2022 to July 2023. Individual interviews with women attending ART outpatient clinics and with clinicians involved in infertility treatment were conducted. Grounded Theory methodology was used to guide data analysis, which entailed a three-step analytical approach. Results: A total of 37 women and five clinicians were interviewed. Incomplete conceptualization of IPV and high mental stress among patients were evident. IPV against women with infertility has been shown to be a specific and severe form of IPV. The phenomenal manifestation of IPV in the context of infertility often takes the form of psychological abuse, including stonewalling, threats of divorce, and non-compliance with ART treatment. Conclusion: The study revealed that the burden of fertility disproportionately falls on women, often intensified by the involvement of other family and cultural norms, exacerbating gender inequalities and IPV risks. Findings from this study calls for an immediate and comprehensive response in healthcare practice. IPV screening, health education, and structural interventions should be introduced to prevent and mitigate this issue. Plain language summary: The interplay between infertility and intimate partner violence (IPV) is a pressing global health issue, yet qualitative evidence on this phenomenon is limited. Therefore, in order to measure and prevent IPV, and to explore and integrate perceptions and experiences from these women with infertility themselves on IPV definition and its phenomenal manifestations in the context of infertility, interviews were conducted with 37 women and five clinicians. The results of this study showed that the phenomenal manifestation of IPV in the context of infertility often takes the form of psychological abuse, including stonewalling, threats of divorce, and non-compliance with ART treatment. The study revealed that the burden of fertility disproportionately falls on women, often intensified by the involvement of other family and cultural norms, exacerbating gender inequalities and IPV risks. [ABSTRACT FROM AUTHOR]

ABSTRACT Aims Design Methods Results Conclusion Implication for the Profession Patient or Public Contribution This study examined the anxiety levels of nurses and nurse candidates regarding humanoid nurse robots and artificial intelligence health technologies in perioperative patient care.Descriptive and cross‐sectional study.The research was conducted with 158 intern students and 167 surgical nurses. Socio‐demographic characteristics form, Questions Form Regarding Humanoid Nurse Robots and Artificial Intelligence Health Technologies, Artificial Intelligence Anxiety Scale and The Medical Artificial Intelligence Preparedness Scale were used. The independent t‐test and one‐way analysis of variance (ANOVA) were used. This study complied with Appendix S1.The total scores on the Artificial Intelligence Anxiety Scale for nurses and nursing students are 73.089 ± 31.667 and 73.624 ± 28.029, respectively. The total scores on the Artificial Intelligence Readiness Scale for nurses and nursing students are 71.736 ± 15.064 and 72.183 ± 13.714, respectively. When comparing the sociodemographic characteristics and scale scores of nurses, a statistically significant difference was found between age and the Artificial Intelligence Anxiety Scale scores (p < 0.05). There was also a statistically significant difference between age, gender and work duration and the Artificial Intelligence Readiness Scale scores for nurses (p < 0.05).Both groups exhibited moderate levels of anxiety and readiness regarding artificial intelligence. Comprehensive research is needed to elucidate the impact of artificial intelligence technologies on nursing professionals.The proper use of Artificial Intelligence technologies can enhance the quality of patient care, alleviate the workload, increase patient and staff satisfaction and foster new perspectives on acceptance. With their integration into clinics, a patient‐centred care environment will emerge, improving patient safety, outcomes and overall well‐being. Thus, the anxieties of nurses and students towards artificial intelligence technologies will decrease, and their readiness will increase.No Patient or Public Contribution. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung and colon cancer are among the leading causes of cancer-related deaths globally, emphasizing the critical need for early and precise detection to improve treatment and patient outcomes. This research introduces a novel deep learning model for efficient lung and colon cancer detection, aiming to address the limitations of existing computationally intensive models. This study proposes a lightweight, parameter-efficient model suitable for mobile devices, utilizing a 1D convolutional neural network enhanced with Squeeze-and-Excitation layers. By achieving 100% accuracy on a large dataset of histopathological images, this research provides a significant advancement in cancer detection technology. The findings hold the potential to revolutionize medical diagnostics by enabling faster, more accessible, and reliable cancer screening, potentially leading to earlier interventions and improved survival rates. Lung and colon cancers are among the leading causes of cancer-related mortality worldwide. Early and accurate detection of these cancers is crucial for effective treatment and improved patient outcomes. False or incorrect detection is harmful. Accurately detecting cancer in a patient's tissue is crucial to their effective treatment. While analyzing tissue samples is complicated and time-consuming, deep learning techniques have made it possible to complete this process more efficiently and accurately. As a result, researchers can study more patients in a shorter amount of time and at a lower cost. Much research has been conducted to investigate deep learning models that require great computational ability and resources. However, none of these have had a 100 % accurate detection rate for these life-threatening malignancies. Misclassified or falsely detecting cancer can have very harmful consequences. This research proposes a new lightweight, parameter-efficient, and mobile-embedded deep learning model based on a 1D convolutional neural network with squeeze-and-excitation layers for efficient lung and colon cancer detection. This proposed model diagnoses and classifies lung squamous cell carcinomas and adenocarcinoma of the lung and colon from digital pathology images. Extensive experiment demonstrates that our proposed model achieves 100 % accuracy for detecting lung, colon, and lung and colon cancers from the histopathological (LC25000) lung and colon datasets, which is considered the best accuracy for around 0.35 million trainable parameters and around 6.4 million flops. Compared with the existing results, our proposed architecture shows state-of-the-art performance in lung, colon, and lung and colon cancer detection. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, digital health solutions ensured the continuity of care especially in primary healthcare practices. COVID-19 accelerated the adoption of digital health solutions. The aim of this study is to describe the digital health-related attitudes and experiences of Hungarian Primary Care Physicians (PCPs) and also analyse the socio-demographic effects on digital health attitudes among PCPs. Methods: This study used a quantitative and qualitative mixed methodological approach to examine the digital health-related attitudes and experience of Hungarian PCPs. As part of the "E-physicians and E-patients in Hungary" survey, we conducted an online survey among medical doctors working in Hungary between July 2021 to May 2022. A total of 1,774 questionnaires were received, consisting of 1,576 medical doctors and 198 dentists. Among the medical doctors there were 415 primary care physicians (PCPs). In addition to the online questionnaire survey, qualitative research in the form of semi-structured interviews with doctors was also conducted. These interviews took place between October 2021 and June 2022. A total of 62 interviews were conducted,19 with PCPs. Results: Primary care physicians are more open to technologies that facilitate communication and collaboration with patients, while showing less interest in technologies that support clinical work. Of the demographic variables (age, gender, type of settlement), age was found to have the most significant effect on digital health knowledge, use and intended use. Both the interviews and the multivariate analysis indicate that individuals with greater knowledge, expertise, and experience are more likely to perceive the advantages of digital solutions. This highlights the importance of training, especially given the significant aging population among Hungarian primary care physicians, who may not be accustomed to using these tools naturally. The way PCPs perceive patient expectations regarding the use of digital health tools has a significant impact on the PCPs' use and intended use of digital tools. When looking at perceived patient needs among PCPs the effect of age and municipality type of PCPs is significant. As age increases, the perception of patient needs decreases (from 5.02 to 4.47), and by municipality type, the average number of perceived needs decreases as one moves from larger cities to smaller municipalities (from 4.85 to 4.14). Conclusions: Digital health solutions have the potential to enhance the work of PCPs, but successful implementation requires addressing specific needs, demographic differences, and challenges faced by PCPs. Development of infrastructure, education, and institutional support is necessary to ensure more efficient and higher-quality healthcare delivery through the use of digital technologies. [ABSTRACT FROM AUTHOR]

Background: The targeted application of cutting-edge high-throughput molecular data technologies provides an enormous opportunity to address key health, economic and environmental issues in the tropics within the One Health framework. The Earth's tropical regions are projected to contain > 50% of the world's population by 2050 coupled with 80% of its biodiversity however these regions are relatively less developed economically, with agricultural productivity substantially lower than temperate zones, a large percentage of its population having limited health care options and much of its biodiversity understudied and undescribed. The generation of high-throughput molecular data and bespoke bioinformatics capability to address these unique challenges offers an enormous opportunity for people living in the tropics. Main: In this review we discuss in depth solutions to challenges to populations living in tropical zones across three critical One Health areas: human health, biodiversity and food production. This review will examine how some of the challenges in the tropics can be addressed through the targeted application of advanced omics and bioinformatics and will discuss how local populations can embrace these technologies through strategic outreach and education ensuring the benefits of the One Health approach is fully realised through local engagement. Conclusion: Within the context of the One Health framework, we will demonstrate how genomic technologies can be utilised to improve the overall quality of life for half the world's population. [ABSTRACT FROM AUTHOR]

The consequences of an aging society pose one of the major challenges of our time. Technology solutions that alleviate the strain on the overburdened healthcare system can effectively support individuals needing assistance. Still, technology acceptance is a crucial variable in understanding what drives users to utilize such applications. This study examined the role of trust and privacy in the accepted use of ambient technologies for health-related purposes. Using an online survey we collected data from N = 102 respondents. Findings revealed that participants are willing to use ambient technology for health. Benefits and barriers are traded off, whereby the advantages outweigh the perceived barriers. Trust conditions to be fulfilled include data security, technical soundness, and health-related aspects. Having control over personal information results as the most important privacy aspect, followed by the supervision of social contacts. Regression analyses revealed perceived motives and barriers, technical soundness, and health-related trust to be significant predictors of acceptance. [ABSTRACT FROM AUTHOR]

In the last year, there have been many remarkable articles published in the Italian Journal of Pediatrics. This review highlights papers that can be potentially helpful in healthcare practice among the most cited or accessed papers on the journal website. We have chosen key articles on allergy, analgesics, cardiology, endocrinology, gastroenterology, genetics, global health, infectious diseases, neonatology, neurology and pulmonology. Advances in understanding risk factors, mechanisms, diagnosis, treatment options and prevention of pediatric diseases have been discussed and in the context of the subsequent steps. We think that progresses achieved in 2023 will have a significant impact on the management of diseases in childhood. [ABSTRACT FROM AUTHOR]

This Medical News story is an interview with Tanzeem Choudhury, PhD, the chair in Integrated Health and Technology at Cornell Tech, about key lessons from the early development of wearable technology and how AI stands to continue innovations. [ABSTRACT FROM AUTHOR]

Health and care information technologies can improve service delivery, but unfortunately many fail to realise their potential. This is because tools fail to effectively integrate with user and organisational practices. We here draw on two decades and an extensive body of practical and theoretical experience of conducting formative evaluations in health service settings to extract seven essential principles for planning and developing digital tools. Firstly, it is important to determine if digitalisation is the right approach for the desired change and to view these tools as complex interventions needing careful planning. The development process should embrace varying levels of co-creation and adapt to different needs and contexts. Ensuring that tools integrate well with existing information infrastructures and striking a balance between being widely applicable and locally relevant is essential. Furthermore, planning for how these interventions will fit into broader health and care pathways is vital. By considering these factors, decision-makers can significantly improve the chances of successful implementation, adoption and scaling of health and care technologies. [ABSTRACT FROM AUTHOR]

Background/Objectives: This systematic and bibliographic review examines publications in the field of dementia and video game research from 2004 to 2023. The main objective is to assess developments and trends in video game technology for dementia care and detection. Methods: The PubMed database was the primary source for publications. PRISMA guidelines were applied to structure this review. Ten variables were defined, investigated, and split into three main categories: bibliographic, medical, and technical. Results: The results were synthesized using a quantitative approach to reduce bias through interpretation. Of 209 initial results, 77 publications have been included in the investigation. More studies focus on rehabilitation over assessment and detection of dementia. Clinical trials are typically conducted with limited participants. The most populated trials rarely enrol over 300 subjects. On average, around 38 subjects were enrolled in the trials. These studies are commonly supervised by a trainer or technology specialist, suggesting a technology gap in familiarity in the trial demographic. Conclusions: Most interventions assessed were custom-designed applications with a specific outcome, focusing on physical activity and cognitive exercises. As the first of its kind, this publication focuses on the technical aspects of applied technologies and development methods. Using video games to treat and detect patients with cognitive impairments like dementia can benefit healthcare professionals, caretakers, and patients. [ABSTRACT FROM AUTHOR]

Background/Objectives: Globally, life expectancy has been increasing with South Korea focusing on improving health to enhance quality of life. The COVID-19 pandemic further emphasized the need for digital transformation in healthcare, accelerating digital health adoption. This study explores the digital divide between 'Digital Natives (20–39 Y)' and 'Digital Immigrants (40–69 Y)', focusing on digital device usage and confidence. Methods: This study utilized national survey data from the Digital Health Literacy Survey Results and Policy Implications, focusing on differences in digital device use and confidence between young adults (20–39 Y) and middle-aged adults (40–69 Y). The participants comprised 1000 adults aged 20 to 69 in the Republic of Korea. Respondents were queried about their use of digital health tools, such as wearable devices and mobile apps. Confidence in using digital systems and managing health via digital tools was assessed using a five-point Likert scale. Results: The findings indicated that while young adults have lower rates of using digital devices for healthcare, they exhibit higher confidence in using such devices. In contrast, middle-aged adults, despite having lower confidence, report higher usage of digital devices for healthcare purposes. Conclusions: This study explored differences in digital confidence and healthcare usage between age groups and aimed to propose effective health management strategies based on digital accessibility. [ABSTRACT FROM AUTHOR]

The accurate identification of medicine vials is crucial for emergency medical services, especially for vials that resemble one another but have different labels, volumes, and concentrations. This study introduces a method to detect vials in real-time using mixed reality technology through Microsoft HoloLens 2. The system is also equipped with an SQL server to manage barcode and vial information. We conducted a comparative analysis of the barcode detection capabilities of the HoloLens 2 camera and an external scanner. The HoloLens 2 effectively identified larger barcodes when they were 20–25 cm away in normal lighting conditions. However, it faced difficulties in detecting smaller barcodes that were consistently detected by the external scanner. The frame rate investigation revealed performance fluctuations: an average of 10.54 frames per second (fps) under standard lighting conditions, decreasing to 10.10 fps in low light and further reducing to 10.05 fps when faced with high barcode density. Resolution tests demonstrated that a screen resolution of 1920 × 1080 yielded the best level of accuracy, with a precision rate of 98%. On the other hand, a resolution of 1280 × 720 achieved a good balance between accuracy 93% and speed. The HoloLens 2 demonstrates satisfactory performance under ideal circumstances; however, enhancements in detecting algorithms and camera resolution are required to accommodate diverse surroundings. This approach seeks to help paramedics make quick and accurate decisions during critical situations and tackle common obstacles such as reliance on networks and human mistakes. Our new approach of a hybrid method that integrates an external Bluetooth scanner with the MR device gives optimal results compared to the scanner-only approach. [ABSTRACT FROM AUTHOR]

Background: Globally, digital transformation has been sweeping through healthcare in recent years. Reflecting this global change, Australia's health and social care sector is also undergoing rapid digitalisation. Digital approaches can enhance care planning and coordination activities, as well as improve efficiencies in documentation and coordination of care. As the aged care environment continues to become digitalised into the future, there is an expectation that nurses practise the delicate art and science of compassionate caregiving in a technology-proliferated environment where care planning, provisioning, and documenting require digital knowledge and skills. Aim: To explore the perspectives and expectations of nurses working in residential aged care setting regarding the utilisation of technology to enhance care at the end-of-life. Methods: A qualitative descriptive research study design based on the secondary analysis of data collected as part of a larger study. Data collection was conducted using six semi-structured interviews and 11 focus group discussions with care workers, nurses, and nursing managers working in Australian residential aged care setting. Results: A total of 64 participants took part in this study. Overall, four themes were generated from the data as following: engagement with various digital systems and platforms; 2) ambivalence toward technology; 3) challenges and concerns in technology use; and 4) anticipated technology roles in end-of-life care. This study found that, nurses in Australian RAC are open to engage with technologies for end-of-life provision, despite some ambivalence and challenges encountered in the process. Conclusion: Nurses in residential aged care have an important role in end-of-life care of many older Australians. Digital approaches offer care and coordination opportunities however require the aged care sector and nurses to take up these opportunities. While nurses demonstrate openness to technology, focus needs to be placed on technology use support. This presents an opportunity for nurses to actively shape the future of digital innovations in aged care, ensuring high-quality, compassionate care for residents in their final stages of life. [ABSTRACT FROM AUTHOR]