Artificial Intelligence Devices Research Articles

The Curriculum in NDT Training Have to Include Basics Information Technology to Accomplish Reliability of Automated NDT Result's

Automation in Non-Destructive Testing (NDT) is a reality of modern manufacturing and quality assurance. Information technology applications are simultaneously developed and applied to different fields of NDT methods. The future of doing reliability checks using automated system is inevitable. The results are important to make a variety of decisions regarding the quality of a product and/or service, hence affecting safety and reliability. Depending on artificial intelligence and quantum device development, various NDT training schemes have to adopt a new curriculum to address the issue of the basic knowledge of information technology and automation in NDT. NDT operators are basically dependent on the end results of software, in which the software developer uses a set of algorithms. These are obtained in three categories; 1) Real time database; 2) Synthetic database and 3) Hybrid or combination of them. The developers should know how the NDT systems run and NDT operators in the other hand know the mechanism of the program, so that appropriate action can be taken, in case there is a missed detection, or a detected indication is not very much detrimental to the reliability of the part (Over sizing or wrongly evaluated). For example, a set of algorithms are used to create the focal laws in a phased array ultrasonic test software in space of 1˚ beam steering. Each has a limit of maximum up to 2nd Critical angle. Operators try to copy the same algorithm and set up to 89˚ refraction angle, which will just create creep wave with unreliable results to detect effective size the reflector. Programming knowledge of the operator can effectively eliminate this type of unreliability. In various Schemes of NDT Syllabus which are in common place to employ and train plus certify operators does not have it; actually, the operators do the scope of work which does includes application of Artificial Intelligence (AI) and Software. With the development of Q coupler (Quantum Computing) system, it is obvious to adopt the upcoming technology into the application of NDT industry. Training needs to update the curriculum accordingly. The current topic has intended to focus on the training gap and to address related kind of unreliability.

Protecting the patient’s data in the 21st century healthcare industry: Is the African continent ready for the digital space?

To ensure proper healthcare services and automation, including minimising medical errors and providing faster and more efficient healthcare, the medical field is experiencing innovative technological trends. They include automated patient records, hospital management system software, telemedicine, and the use of artificial intelligence devices. These innovations exist in more than just the global north, as they are slowly finding residence in African countries. Focusing on automated patient record, the article examines existing data protection legal frameworks in Africa to ascertain whether they provide effective remedy mechanisms patients can access should a breach occur. Taking cognisance of the current African Continental Free Trade Area Agreement (AfCFTA) aimed at creating a single market for goods and services; it is evident that healthcare services might have a continental approach. Thus, the article adopts a continental, regional, and national perspective. Using doctrinal method, the article compares the African Union Data Protection Convention with the European Union General Data Protection Regulation to draw lessons from the European Union Experience. The article finds that strengthened national mechanisms might provide the requisite remedy mechanisms patients can access to ensure enforcement of their rights to data protection.

Tough bilayer thermo‐responsive hydrogel actuator reinforced by Fe3+ complexation

AbstractHydrogel actuators usually suffer from the poor mechanical property, which hinders their wide applications. In this study, we propose a tough bilayer hydrogel actuator that can respond quickly to temperature. The hydrogels are composed of poly(N‐isopropylacylamide) (PNIPAM) layer and poly(acrylamide‐co‐acrylic acid) (P(AAm‐co‐AAc)) layer strengthened through Fe3+ complexation with carboxyl groups. By optimizing the PNIPAM content, the resulting bilayer hydrogel P(NIPAM0.9/AAm‐co‐AAc)‐Fe3+ exhibits fast and large‐amplitude bending and recovery in response to temperature. It also shows excellent mechanical properties with a tensile strength of 2.54 MPa, tensile modulus of 3.7 MPa, and toughness of 9.4 MJ/m3, respectively. Besides, the multiple noncovalent interactions within the hydrogels, including Fe+‐mediated coordination and hydrogen bonds, can serve as dynamic but stable associations, leading to a good self‐healing ability. The bilayer hydrogel is further made into a gripper that can effectively and sensitively respond to temperature to capture and release an object, showing its potential application in artificial intelligent devices.

MXene-Based Soft Humidity-Driven Actuator with High Sensitivity and Fast Response.

Soft actuators possessing notable mechanical deformations, high sensitivity, and fast response speed play a crucial role in various applications, such as artificial muscles, soft robots, and intelligent devices. In this study, a smart humidity-driven actuator was successfully fabricated by utilizing MXene/cellulose nanofiber (CNF)/LiCl (MCL) through vacuum-assisted filtration with fast response speed and high sensitivity. Utilizing the excellent humidity responsiveness of MXene/CNF and the robust hygroscopicity of LiCl, the synergistic effect of these materials enhances the hygroscopic properties and response speed of the actuator. The MCL actuator demonstrates excellent actuation performance, fast deformation, and reliable cyclic stability. To illustrate the extensive potential of the soft actuator, a range of applications, from bionic devices to soft grippers and crawling actuators, are showcased. Remarkably, the crawling actuator demonstrates sustained crawling motion without necessitating a humidity switch, relying on the humidity gradient from water droplets, and exhibits spontaneous directional motions within a certain range, which makes it a promising prospect in the field of soft robotics.

Artificial Intelligence, data protection and medical device regulations: squaring the circle with a historical perspective in Europe

Abstract Purpose This manuscript presents a parallel among important EU legal frameworks, such as the Medical Device Regulations and General Data Protection Regulation and economic-historical challenges faced by European citizens. This parallel offers a prospective for understanding the direction taken by policymakers for forthcoming regulations, such as the European Health Data Space and the AI-Act. Method The paper describes key commonalities and differences among those regulations, which may help scholars working with AI in medicine, navigating this articulated legal framework. Results The European Union journey started with the European Coal and Steel Community, enforced after the WWII for nurturing European physical reconstruction and as a mean for peacekeeping: without coil it was not possible to transform steel, and with shared control of steel it was not possible for individual member states to build war weapons. Conclusions In this century, modern coal are data and modern steel is the AI, which is a crucial enabler for European economy growth and competitiveness, and, potentially, a powerful war weapon. Ensuring a fair data-sharing and correct use of AI is crucial for sustainable development, peace, and democracy in Europe and beyond.

Use of an Artificial Intelligence Device for Evaluating Blood Loss in Complex Major Orthopaedic Surgery Procedures

BackgroundAn artificial intelligence algorithm that analyzes the pulse oximeter waveform in the fingertip can be used to determine the compensatory reserve index (CRI) in trauma patients. This measurement shows the remaining cardiovascular capacity and is known to be more specific and sensitive in detecting blood loss than are routine vital signs. We hypothesized that the CRI measurement could predict loss of reserve cardiovascular capacity in patients undergoing major orthopaedic surgery, and therefore could help in their management. MethodsA total of 304 patients undergoing lower extremity arthroplasty consented to participate in waveform monitoring. Pulse oximeter waveforms were sensed with a fingertip probe and processed with a tablet computer that remained with the patient during surgery and recovery in the hospital. The CRI, systolic blood pressure (SBP), and heart rate (HR) were evaluated throughout the postoperative period. ResultsThe CRI measurement identified a group of patients who were significantly more likely to require transfusions and emergency medical care (P = 0.000021). Patients who had morbid obesity were especially likely to have low CRI results and a high percentage of clinical events. A CRI of 0.40 or higher was evaluated retrospectively as the criterion for withholding transfusion in 54 patients, but that group had a significantly higher incidence of transfusion later in treatment than did the cohort as a whole. The SBP and HR were not useful in predicting the need for transfusion until late in treatment. ConclusionsThis study suggests that the CRI measurement can identify patients at risk for transfusion and the need for urgent medical care and may aid in the management of blood loss and transfusion in major orthopaedic surgery.

TPTM-HANN-GA: A Novel Hyperparameter Optimization Framework Integrating the Taguchi Method, an Artificial Neural Network, and a Genetic Algorithm for the Precise Prediction of Cardiovascular Disease Risk

The timely and precise prediction of cardiovascular disease (CVD) risk is essential for effective prevention and intervention. This study proposes a novel framework that integrates the two-phase Taguchi method (TPTM), the hyperparameter artificial neural network (HANN), and a genetic algorithm (GA) called TPTM-HANN-GA. This framework efficiently optimizes hyperparameters for an artificial neural network (ANN) model during the training stage, significantly enhancing prediction accuracy for cardiovascular disease (CVD) risk. The proposed TPTM-HANN-GA framework requires far fewer experiments than a traditional grid search, making it highly suitable for application in resource-constrained, low-power computers, and edge artificial intelligence (edge AI) devices. Furthermore, the proposed TPTM-HANN-GA framework successfully identified the optimal configurations for the ANN model’s hyperparameters, resulting in a hidden layer of 4 nodes, a tan h activation function, an SGD optimizer, a learning rate of 0.23425849, a momentum rate of 0.75462782, and seven hidden nodes. This optimized ANN model achieves 74.25% accuracy in predicting the risk of cardiovascular disease, which exceeds the existing state-of-the-art GA-ANN and TSTO-ANN models. The proposed TPTM-HANN-GA framework enables personalized CVD prediction to be efficiently conducted on low-power computers and edge-AI devices, achieving the goal of point-of-care testing (POCT) and empowering individuals to manage their heart health effectively.

Forming-less flexible memristor crossbar array for neuromorphic computing applications produced using low-temperature atomic layer deposition

The rapid development of wearable artificial intelligence devices based on memristor crossbar arrays has increased the demand for flexible electronics. However, fabricating crossbar arrays on flexible substrates faces inherent challenges, notably due to the complex fabrication process at low temperatures. Moreover, ensuring the stability and reliability of memristor device remains a crucial issue. To address these issues, this study introduced N-doped TaOx (N:TaOx) as a flexible memristor crossbar array fabricated via atomic layer deposition directly on a flexible substrate at an exceptionally low temperature (150 °C). The flexible PET/ITO/N:TaOx/TiN memristor device exhibited forming-less bipolar resistive switching properties with analog memory characteristics. This was accomplished by increasing the nitrogen doping concentration and effectively reducing oxygen-related defects in TaOx. The amorphous-phase flexible N:TaOx memristor demonstrated remarkable stability, enduring 500 switching cycles and retaining its state for 24 h during bending tests involving 104 bending cycles at 2.5 mm bending radius. Furthermore, a 6 × 6 flexible memristor crossbar array was fabricated successfully, with all 36 devices exhibiting well-defined memristor behavior and minimal variation attributed to the film's uniformity achieved through ALD process. Additionally, by programming the tunable conductance of each device in the array, characters of desired shapes can be formed and read. Regarding their synaptic behavior via long-term potentiation and depression, the proposed flexible memristor device showed an outstanding accuracy of 96.44 % in image recognition tasks under extreme bending conditions, employing the MNIST and Fashion-MNIST datasets. The N:TaOx memristor deposited at low temperatures thus exhibits significant potential for use in wearable neuromorphic hardware applications due to its high density, high performance, reliability, and precise image recognition.

A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography.

Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast cancer and reduce the number of false-positive examinations. Materials and Methods The deep learning algorithm was trained using 123 248 two-dimensional digital mammograms (6161 cancers) and a retrospective study was performed on three nonoverlapping datasets of 14 831 screening mammography examinations (1026 cancers) from two U.S. institutions and one U.K. institution (2008-2017). The stand-alone performance of humans and AI was compared. Human plus AI performance was simulated to examine reductions in the cancer detection rate, number of examinations, false-positive callbacks, and benign biopsies. Metrics were adjusted to mimic the natural distribution of a screening population, and bootstrapped CIs and P values were calculated. Results Retrospective evaluation on all datasets showed minimal changes to the cancer detection rate with use of the AI device (noninferiority margin of 0.25 cancers per 1000 examinations: U.S. dataset 1, P = .02; U.S. dataset 2, P < .001; U.K. dataset, P < .001). On U.S. dataset 1 (11 592 mammograms; 101 cancers; 3810 female patients; mean age, 57.3 years ± 10.0 [SD]), the device reduced screening examinations requiring radiologist interpretation by 41.6% (95% CI: 40.6%, 42.4%; P < .001), diagnostic examinations callbacks by 31.1% (95% CI: 28.7%, 33.4%; P < .001), and benign needle biopsies by 7.4% (95% CI: 4.1%, 12.4%; P < .001). U.S. dataset 2 (1362 mammograms; 330 cancers; 1293 female patients; mean age, 55.4 years ± 10.5) was reduced by 19.5% (95% CI: 16.9%, 22.1%; P < .001), 11.9% (95% CI: 8.6%, 15.7%; P < .001), and 6.5% (95% CI: 0.0%, 19.0%; P = .08), respectively. The U.K. dataset (1877 mammograms; 595 cancers; 1491 female patients; mean age, 63.5 years ± 7.1) was reduced by 36.8% (95% CI: 34.4%, 39.7%; P < .001), 17.1% (95% CI: 5.9%, 30.1%: P < .001), and 5.9% (95% CI: 2.9%, 11.5%; P < .001), respectively. Conclusion This work demonstrates the potential of a semiautonomous breast cancer screening system to reduce false positives, unnecessary procedures, patient anxiety, and medical expenses. Keywords: Artificial Intelligence, Semiautonomous Deep Learning, Breast Cancer, Screening Mammography Supplemental material is available for this article. Published under a CC BY 4.0 license.

Biomimetic Electronic Skin Based on a Stretchable Ionogel Mechanoreceptor Composed of Crumpled Conductive Rubber Electrodes for Synchronous Strain, Pressure, and Temperature Detection.

Electronic skin (e-skin) is showing a huge potential in human-computer interaction, intelligent robots, human health, motion monitoring, etc. However, it is still challenging for e-skin to realize distinguishable detection of stretching strain, vertical pressure, and temperature through a simple noncoupling structure design. Here, a stretchable multimodal biomimetic e-skin was fabricated by integrating layer-by-layer self-assembled crumpled reduced graphene oxide/multiwalled carbon nanotubes film on natural rubber (RGO/MWCNTs@NR) as stretchable conductive electrodes and polyacrylamide/NaCl ionogel as a dielectric layer into an ionotropic capacitive mechanoreceptor. Unlike natural skin receptors, the sandwich-like stretchable ionogel mechanoreceptor possessed a distinct ionotropic capacitive behavior for strain and pressure detection. The results showed that the biomimetic e-skin displayed a negative capacitance change with superior stretchability (0-300%) and a high gauge factor of 0.27 in 180-300% strain, while exhibiting a normal positive piezo-capacitance behavior in vertical pressure range of 0-15 kPa with a maximal sensitivity of 1.759 kPa-1. Based on this feature, the biomimetic e-skin showed an excellent synchronous detection capability of planar strain and vertical pressure in practical wearable applications such as gesture recognition and grasping movement detection without a complicated mathematical or signal decoupling process. In addition, the biomimetic e-skin exhibited a quantifiable linear responsiveness to temperature from 20-90 °C with a temperature coefficient of 0.55%/°C. These intriguing properties gave the biomimetic e-skin the ability to perform a complete function similar to natural skin but beyond its performance for future wearable devices and artificial intelligence devices.

Empirical validation of federated learning with YOLO v7-tiny for road sign detection: A simulation-based comparative study

Detecting road signs is a critical component in the development of intelligent driving systems. While centralized machine learning approaches have demonstrated potential in this field, the untapped potential of Federated Learning warrants exploration. This research aims to bridge this gap by examining the feasibility of applying Federated Learning within edge Artificial Intelligence (AI) computing environments for the purpose of road sign detection. Utilizing the You Only Look Once (YOLO) v7-tiny model and a range of experimental parameters demonstrates that Federated Learning is viable and outperforms centralized approaches under specific conditions. The study's empirical analysis highlights the sensitivity of detection accuracy to varying experimental parameters. The study contributes to the existing literature by establishing the efficacy of Federated Learning in road sign detection, particularly in edge AI settings constrained by hardware limitations and privacy concerns. However, the study acknowledges limitations, including the lack of deployment on actual edge AI devices and a restricted range of experimental parameters. Future research should aim for more exhaustive experiments with broader datasets, diverse parameters, and real-world edge AI environments. These findings offer valuable insights for future implementations in intelligent automotive systems.

Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes

INTRODUCTION: With the rapid development of artificial intelligence technology, wearable artificial intelligence devices show great potential in medical rehabilitation. This study explores the Real Time monitoring effect of AI wearable devices in the rehabilitation process of track and field athletes. The application of this technology in rehabilitation monitoring was investigated through the introduction of advanced sensing technology and data analysis algorithms to provide track and field athletes with more scientific and personalized rehabilitation programs. OBJECTIVES: A group of track and field athletes was selected as the research object and equipped with an artificial intelligence wearable device, which is capable of Real Time monitoring of the athletes' physiological parameters, sports postures, joint mobility, and other rehabilitation-related data. An individualized rehabilitation model was established through the data collected by these sensors, and advanced artificial intelligence algorithms were used to analyze the data in Real Time. At the same time, the sensor data were combined with the actual performance of the athletes' rehabilitation training to comprehensively assess the effectiveness of AI wearable devices in rehabilitation monitoring. METHODS: This study aims to assess the effect of Real Time monitoring of AI wearable devices in the rehabilitation of track and field athletes and to explore their potential application in the rehabilitation process. Real Time tracking of athletes' physiological status and athletic performance aims to provide more accurate and timely information to rehabilitation doctors and coaches to optimize the rehabilitation training program and promote the rehabilitation process of athletes. RESULTS: The study showed that artificial intelligence wearable devices have significant Real Time monitoring effects in rehabilitating track and field athletes. Through Real Time monitoring of data such as physiological parameters, sports posture, and joint mobility, the rehabilitation team was able to identify potential problems and adjust the rehabilitation program in a more timely manner. Athletes using artificial intelligence wearable devices improved the personalization and targeting of rehabilitation training, and the rehabilitation effect was significantly better than that of traditional monitoring methods. CONCLUSION: This study concludes that artificial intelligence wearable devices perform well in rehabilitating track and field athletes, providing a more scientific and comprehensive means of rehabilitation monitoring. Through Real Time tracking, the rehabilitation team could better understand the rehabilitation progress of the athletes, adjust the rehabilitation program in a targeted manner, and improve the rehabilitation effect. However, future research still needs to optimize the performance of the devices further, expand the sample size, and thoroughly study the monitoring needs at different stages of rehabilitation to better meet the individualized requirements of track and field athletes' rehabilitation process.

Possibilities of Using Artificial Intelligence in EU and UN Peacekeeping Activities

Abstract The main purpose of writing this article was to show how disruptive technologies, including artificial intelligence and machine learning, can be used in military operations. We also covered how the UN and other international organisations have begun to regulate this rapidly exploding field from a policy perspective. We presented some of the technologies that use AI and made suggestions for their general military application and how these technologies can be used in peacetime operations. Artificial intelligence and intelligent devices can also bring enormous benefits in the areas of command and control systems, reconnaissance and intelligence activities. We also presented issues and dilemmas related to developments in this direction, such as ethical, liability, security and information protection problems and dilemmas.

Single flake homo p–n diode of MoTe2 enabled by oxygen plasma doping

Abstract Two-dimensional (2D) materials play a crucial role as fundamental electrical components in modern electronics and optoelectronics next-generation artificial intelligent devices. This study presents a methodology for creating a laterally uniform p–n junction by using a partial oxygen plasma-mediated strategy to introduce p-type doping in single channel MoTe2 device. The MoTe2 field effect transistors (FETs) show high electron mobility of about ∼23.54 cm2 V−1 s−1 and a current ON/OFF ratio of ∼106 while p-type FETs show hole mobility of about ∼9.25 cm2 V−1 s−1 and current ON/OFF ratio ∼105 along with artificially created lateral MoTe2 p–n junction, exhibited a rectification ratio of ∼102 and ideality factor of ∼1.7 which is proximity to ideal-like diode. Thus, our study showed a diversity in the development of low-power nanoelectronics of next-generation integrated circuits.

BIG DATA ANALYTICS IN HEALTHCARE: A REVIEW OF RECENT ADVANCES AND POTENTIAL FOR PERSONALIZED MEDICINE

This comprehensive review explores the profound impact of big data analytics on healthcare, focusing on its potential to revolutionize personalized medicine. Big data analytics, driven by technological innovations such as genomic sequencing, artificial intelligence, and wearable devices, offers transformative opportunities for precision diagnostics, treatment customization, and predictive healthcare. Despite the promises, challenges persist, including data privacy concerns, ethical considerations, and technical complexities. Overcoming these hurdles necessitates robust cybersecurity measures, bias mitigation strategies, enhanced data quality, and infrastructure and workforce development investments. Moreover, with updated regulations and ethical guidelines, healthcare policy and practice must adapt to accommodate the evolving digital landscape. Collaboration across disciplines and ongoing research and innovation are essential to fully harness the benefits of big data analytics, leading to improved patient care and healthcare system efficiency.&#x0D; Keywords: Big Data Analytics, Personalized Medicine, Healthcare Innovation, Data Privacy, Ethical Considerations, Precision Diagno. stics

Consumer attitude toward using artificial intelligence (AI) devices in hospitality services

PurposeThe study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.Design/methodology/approachThis study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.FindingsThe finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.Practical implicationsThis study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.Originality/valueThis study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.

Exploring tourist perceptions of artificial intelligence devices in the hotel industry: impact of industry 4.0

ABSTRACT This study explores the perceptions of tourists regarding the use of artificial intelligence (AI) devices in the hotel industry. Adopting the artificially intelligent device use acceptance (AIDUA) framework, this research uncovered the factors that influence tourists’ attitudes towards AI devices and their willingness/unwillingness to use them. This study also demonstrated the moderating impact of Industry 4.0. The strong associations among social influence, hedonic motivation, anthropomorphism, performance expectancy, effort expectancy, users’ emotions towards AI device usage, and their willingness/unwillingness to accept AI devices are explored. Furthermore, Industry 4.0 was of utmost importance in strengthening the relations among research constructs.

The role of digital skills in the acceptance of artificial intelligence

Purpose The service industry is facing the huge impact of digital transformation, in which artificial intelligence (AI) plays one of the most important roles. This study aims to expand the understanding of the AI acceptance framework and confirm whether consumers’ digital skills have a moderating effect on the research model. Design/methodology/approach Hypotheses were tested using a data set of 1,641 individuals. Partial least squares structural equation modeling and multi-group analysis were used to estimate the model. Findings The results indicate that antecedent factors influence consumers’ willingness to use AI devices in services. The two groups of different digitally savvy respondents differ because the influence of anthropomorphism, social influence and hedonic motivation on respondents’ perceived efforts to use AI devices in service delivery depends on respondents’ digital skills. Originality/value The novel contribution of this study is reflected in a comprehensive model that explains the moderating effect of individual digital skills on willingness to use AI devices. The attitudes of experienced and digitally skilled consumers are valuable and highlight some important theoretical, practical implications and future lines of research.

Endocuff With or Without Artificial Intelligence-Assisted Colonoscopy in Detection of Colorectal Adenoma: A Randomized Colonoscopy Trial.

Both artificial intelligence (AI) and distal attachment devices have been shown to improve adenoma detection rate and reduce miss rate during colonoscopy. We studied the combined effect of Endocuff and AI on enhancing detection rates of various colonic lesions. This was a 3-arm prospective randomized colonoscopy study involving patients aged 40 years or older. Participants were randomly assigned in a 1:1:1 ratio to undergo Endocuff with AI, AI alone, or standard high-definition (HD) colonoscopy. The primary outcome was adenoma detection rate (ADR) between the Endocuff-AI and AI groups while secondary outcomes included detection rates of polyp (PDR), sessile serrated lesion (sessile detection rate [SDR]), and advanced adenoma (advanced adenoma detection rate) between the 2 groups. A total of 682 patients were included (mean age 65.4 years, 52.3% male), with 53.7% undergoing diagnostic colonoscopy. The ADR for the Endocuff-AI, AI, and HD groups was 58.7%, 53.8%, and 46.3%, respectively, while the corresponding PDR was 77.0%, 74.0%, and 61.2%. A significant increase in ADR, PDR, and SDR was observed between the Endocuff-AI and AI groups (ADR difference: 4.9%, 95% CI: 1.4%-8.2%, P = 0.03; PDR difference: 3.0%, 95% CI: 0.4%-5.8%, P = 0.04; SDR difference: 6.4%, 95% CI: 3.4%-9.7%, P < 0.01). Both Endocuff-AI and AI groups had a higher ADR, PDR, SDR, and advanced adenoma detection rate than the HD group (all P < 0.01). Endocuff in combination with AI further improves various colonic lesion detection rates when compared with AI alone.

Recent Advances and Future Directions in Syncope Management: A Comprehensive Narrative Review

Syncope is a highly prevalent clinical condition characterized by a rapid, complete, and brief loss of consciousness, followed by full recovery caused by cerebral hypoperfusion. This symptom carries significance, as its potential underlying causes may involve the heart, blood pressure, or brain, leading to a spectrum of consequences, from sudden death to compromised quality of life. Various factors contribute to syncope, and adhering to a precise diagnostic pathway can enhance diagnostic accuracy and treatment effectiveness. A standardized initial assessment, risk stratification, and appropriate test identification facilitate determining the underlying cause in the majority of cases. New technologies, including artificial intelligence and smart devices, may have the potential to reshape syncope management into a proactive, personalized, and data-centric model, ultimately enhancing patient outcomes and quality of life. This review addresses key aspects of syncope management, including pathogenesis, current diagnostic testing options, treatments, and considerations in the geriatric population.