Artificial Intelligence Devices Research Articles

Hotel AI service: Are employees still needed?

This research adopts the artificial intelligence (AI) device usage acceptance (AIDUA) model and draws on cognitive appraisal theory. It articulates a mediated relationship leading from social influence, hedonic motivation, and anthropomorphism to acceptance of or objection to AI device usage through the mediation of performance expectancy, effort expectancy, and customer emotion in the context of smart hotels. The study further draws on two-factor theory of emotion to identify the moderation of employee presence. Based on a survey design, our research adds to the literature by underscoring why human staff is still critical even in the smart service encounter, especially when customers are looking for superior performance with low effort when using AI devices. However, the presence of employees may be a hindrance in the low performance and high effort expectancies conditions. This research pinpoints a more precise mechanism of the role of employees and technology in delivering intended emotional valence.

College Teachers’ Anxiety Towards Artificial Intelligence: A Comparative Study

Today’s technological development advances in the new form of technology that based on Artificial Intelligence (AI) which predicts the future nature of educational technology. These AI techniques are giving an opportunity to provide teaching learning process more flexible like anytime and anywhere. Since till now in education system teacher play a fundamental role, so cope up with this new AI oriented changing world where quality enhancement of whole system of education is prioritized, therefore technological acceptance and adjustment are two essential criteria. Thus, anxiety level of teachers is important factor for fulfilling these two criteria for using AI devices in instructional process. So, AI – Anxiety (AIA) of teachers play a vital role for proper application of AI technologies in the field of education (AIED). The objectives were to compare mean scores of AIA of College Teachers according to their Gender and Year wise teaching experiences respectively. The sample comprised of 113 college teachers selected through Stratified Random Sampling Method. Out of 113, 51 were males and 62 were females. Besides that, according to their teaching experience 55 were having 0-7 yrs., 28 were having 8-15 yrs., and 30 were having above 15 yrs., respectively. For the purpose of data collection Demographic Information Sheet and Artificial Intelligence Anxiety Scale (AIAS) were used and put to appropriate data analysis. The findings of the study were AIA of College Teachers did not differ significantly according to their Gender and Year wise Teaching Experiences respectively.

OC-004 CAN AI HELP IN EARLY PREDICTION OF SURGICAL SITE INFECTIONS?

Abstract Background The use of Artificial Intelligence (AI) in healthcare has the potential to transform medical diagnosis, including the detection of surgical site infections (SSI). Real-time monitoring of surgical sites and early detection of infection through AI can lead to prompt treatment, improving patient outcomes and reducing healthcare costs and spread of infections in healthcare facilities. Aim This study aimed to determine the use of AI in the early detection of surgical site infections and its impact on patient recovery. Materials and methods The study analysed patients admitted to the hospital over the past year with SSIs. A novel AI device was used to image the wound and detect presence of infection through fluorescence lights that identify bacteria through bioluminescence. Targeted tissue cultures were taken, antibiotics were administered. Some wounds required targeted debridement. Daily dressings were done followed by secondary suturing. Results A total of 34 patients were included in the study, with a mean age of 52.4 years and 14 female and 20 male patients. 25 patients reported pus from the wound, 9 had serous discharge, and 19 had slough present. In 85.2% of patients, the AI device's gram stain for bacteria in the wound was similar to the tissue culture report. The mean time for wound healing was 39.52 days. Conclusion Our study suggests that AI devices can accurately identify the gram stain of bacteria in a wound, leading to prompt antibiotic treatment for patients with SSIs. This could have significant benefits in improving patient outcomes and healthcare costs.

The effect of robot anthropomorphism on revisit intentions after service failure: a moderated serial mediation model

PurposeThe purpose of this study is to examine how robotic anthropomorphism and personalized design may affect consumers' reactions to brands after service failure.Design/methodology/approachThis study conducted two studies based on cognitive appraisal theory and artificial intelligence device acceptance theory. Study 1 explored the mechanisms by which the type of anthropomorphic design of the service robot (humanoid robot/nonhumanoid robot) influenced revisit intention after service failure through a one-factor between-subjects design based on a restaurant dining scenario. Study 2 was based on a hotel check-in scenario and explored the moderating effect of robot personalization design on the above mechanisms through a 2 (anthropomorphic design: humanoid robot/nonhumanoid robot) × 2 (personalized design: self-name/no name) between-subjects design.FindingsStudy 1 shows that consumers have higher performance expectations for nonhumanoid robots, leading to a higher tolerance for service failure, which in turn generates higher revisit intentions. Study 2 shows that consumers' performance expectations are significantly enhanced after custom naming of humanoid robots, so the serial mediation mechanism for the effect of robot anthropomorphic design on revisit intention does not hold.Originality/valueThis study extends the research of artificial intelligence device acceptance theory in the field of service failure and exploratively proposes an intervention mechanism for the negative effects of the anthropomorphic design of service robots.

Application of artificial intelligence wearable devices based on neural network algorithm in mass sports activity evaluation

Based on the rapid development of big data, cloud computing, Internet of things and other technologies in recent years, intelligent hardware devices has been applied to all aspects of life. Under this background, some scholars have put forward relevant concepts such as “Smart Life”. In the field of mass sports life, through the development and application of science and technology, there has been new changes related to the application of neural network algorithm technology and intelligent hardware devices. Therefore, artificial intelligence wearable devices based on wearable technology came into being. This paper analyzes the application of this device in mass sports activities. Then, this paper describes the key research technologies of motion data processing based on neural network algorithm, including: depth frame differential convolution neural network structure, motion data extraction method, human motion signal processing algorithm, etc.; then it analyzes the action recognition and interaction system design based on Intelligent wearable devices. Finally, it analyzes the recognition results of human action system, the accuracy of human action recognition system and the factors that affect the performance of the recognition system. It is concluded that the artificial intelligent wearable devices designed in this paper can be well used in popular sports activities. Finally, it introduces the research on the evaluation strategy of popular sports activities based on artificial intelligence, and hopes that this equipment can help public sports activities. This paper studies the neural network algorithm and applies it to the design process of artificial intelligence wearable devices, which promotes the development of mass sports activity evaluation.

Polyborosiloxane-Encapsulated Carbon Nanotube/Polyurethane Foams for Multifunctional Pressure Memory and Pain Bionic Sensors

Electronic skin (e-skin) is a bionic human skin material that is widely used in artificial intelligence devices. Pressure sensors, as the main component of e-skin, can perceive active pressure spatial distribution in real time. However, in addition to perceiving touch and pressure, human skin also has the ability to sense pain after being impacted or heavily pressed. Therefore, it is hoped that e-skin has the ability to sense the occurrence and disappearance of pain. Here, a pressure memory sensor (PM sensor) based on pressure memory foam (PM foam) was fabricated by a simple and easy-to-scale preparation process. PM foam exhibited excellent electrical conductivity due to the dense three-dimensional conductive network formed by carbon nanotubes (CNTs). PM sensors can achieve pressure memory and spontaneous recovery performance due to self-adhesion between the cell walls provided by polyborosiloxane (PBS). We investigated the effects of CNT and PBS contents on PM sensor sensitivity and memory time and realized the adjustment of sensitivity (0.012–0.099 kPa–1) and memory time (54–325 s). The sensor can bionically sense pain under heavy pressure or impact. In addition, PM foam exhibited excellent electromagnetic interference shielding performance and achieved a shielding effectiveness of more than 30 dB. PM foam realized the intelligent regulation of electromagnetic wave reflection and electromagnetic wave absorption through the conversion of a foam state and solid state. PM foam had excellent Joule heating performance to resist extreme cold environments. Multifunctional pressure memory and pain bionic sensors have great significance and application prospects in e-skin.

Foundation models for generalist medical artificial intelligence.

The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities. Here we identify a set of high-impact potential applications for GMAI and lay out specific technical capabilities and training datasets necessary to enable them. We expect that GMAI-enabled applications will challenge current strategies for regulating and validating AI devices for medicine and will shift practices associated with the collection of large medical datasets.

A Review of the Technology, Training, and Assessment Methods for the First Real-Time AI-Enhanced Medical Device for Endoscopy.

Artificial intelligence (AI) has the potential to assist in endoscopy and improve decision making, particularly in situations where humans may make inconsistent judgments. The performance assessment of the medical devices operating in this context is a complex combination of bench tests, randomized controlled trials, and studies on the interaction between physicians and AI. We review the scientific evidence published about GI Genius, the first AI-powered medical device for colonoscopy to enter the market, and the device that is most widely tested by the scientific community. We provide an overview of its technical architecture, AI training and testing strategies, and regulatory path. In addition, we discuss the strengths and limitations of the current platform and its potential impact on clinical practice. The details of the algorithm architecture and the data that were used to train the AI device have been disclosed to the scientific community in the pursuit of a transparent AI. Overall, the first AI-enabled medical device for real-time video analysis represents a significant advancement in the use of AI for endoscopies and has the potential to improve the accuracy and efficiency of colonoscopy procedures.

Enhanced Polarization Effect of Flexible Magnetoelectric PVDF-TrFE/Fe3O4 Smart Nanocomposites for Nonvolatile Memory Application

High power consumption of nonvolatile memory is a major challenge, as it reduces the memory efficiency of information storage devices. The magnetoelectric (ME) coupling in multiferroic nanocomposites, which can be utilized in magnetoelectric random access memory, is an effective approach to reduce power consumption in information storage. Here, a type of ME nanocomposite embedded with 0.5 wt % Fe3O4 is presented, exhibiting higher ME voltage coefficients for piezoelectric thin films. Specifically, the ME voltage coefficient of the P(VDF-TrFE)/Fe3O4 composite developed in this study is 8.97 mV/(cm·Oe), which is 17.5% higher compared to that of the pure P(VDF-TrFE) at a Hdc of 1000 Oe. Meanwhile, the enhanced ME effect of smart nanocomposites is characterized by the increase of diffraction peak intensity at a microscopic level. The nanocomposite films exhibit high ME voltage coefficients and information storage performance, providing a great potential for creating next-generation memory devices in the realm of artificial intelligence and wearable devices.

External Validation of an Artificial Intelligence Device for Intracranial Hemorrhage Detection

Artificial intelligence applications have gained traction in the field of cerebrovascular disease by assisting in the triage, classification, and prognostication of both ischemic and hemorrhagic stroke. The Caire ICH system aims to be the first device to move into the realm of assisted diagnosis for intracranial hemorrhage (ICH) and its subtypes. A single-center retrospective dataset of 402 head noncontrast CT scans (NCCT) with an intracranial hemorrhage were retrospectively collected from January 2012 to July 2020; an additional 108 NCCT scans with no intracranial hemorrhage findings were also included. The presence of an ICH and its subtype were determined from the International Classification of Diseases-10 code associated with the scan and validated by an expert panel. We used the Caire ICH vR1 to analyze these scans, and we evaluated its performance in terms of accuracy, sensitivity, and specificity. We found the Caire ICH system to have an accuracy of 98.05% (95% confidence interval [CI]: 96.44%-99.06%), a sensitivity of 97.52% (95% CI: 95.50%-98.81%), and a specificity of 100% (95% CI: 96.67%-100.00%) in the detection of ICH. Experts reviewed the 10 incorrectly classified scans. The Caire ICH vR1 algorithm was highly accurate, sensitive, and specific in detecting the presence or absence of an ICH and its subtypes in NCCTs. This work suggests that the Caire ICH device has potential to minimize clinical errors in ICH diagnosis that could improve patient outcomes and current workflows as both a point-of-care tool for diagnostics and as a safety net for radiologists.

Self-healing, EMI shielding, and antibacterial properties of recyclable cellulose liquid metal hydrogel sensor

Flexible hydrogels are promising materials for the preparation of artificial intelligence electronics and wearable devices. Introducing a rigid conductive material into the hydrogels can improve their electrical conductivities. However, it may have poor interfacial compatibility with the flexible hydrogel matrix. Therefore, we prepared a hydrogel containing flexible and highly ductile liquid metal (LM). The hydrogel can be used as a strain sensor to monitor human motion. The hydrogel showed many properties (i.e., recyclability, EMI shielding properties (33.14 dB), antibacterial (100 %), strain sensitivity (gauge factor = 2.92), and self-healing) that cannot be achieved simultaneously by a single hydrogel. Furthermore, the recycling of LM and their application to hydrogel-based EMI shielding materials have not been investigated previously. Due to its excellent properties, the prepared flexible hydrogel has great potential for applications in artificial intelligence, personal healthcare, and wearable devices.

The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy

Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area. A prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group). None of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies. Based on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data.

The dark side of artificial intelligence in service: The “watching-eye” effect and privacy concerns

The potential privacy issues associated with artificial intelligence (AI) in service delivery require careful attention but remain understudied. Through the lens of the watching-eye effect, this research examines the impact of AI on customers’ uneasiness through the mediation of privacy concerns. Study 1 confirms the watching “camera eye” effect of AI devices. Moreover, it identifies the service setting as a contextual boundary condition, wherein this effect holds in a private setting but not in a public setting. Study 2 further indicates that when there is a built-in camera, the “physical eye” in an AI device matters; humanoid AI devices trigger stronger privacy concerns than nonhumanoid AI devices and tablets, leading to greater uneasiness. This impact affects both genders but is more pronounced among women. This research extends the AI literature in service and business ethics and offers insight into managing personal information and privacy issues effectively.

A unique, flexible, and porous pressure sensor with enhanced sensitivity and durability by synergy of surface microstructure and supercritical fluid foaming

Flexible thermoplastic polyurethane (TPU)/multi-walled carbon nanotube (MWCNT) nanocomposite piezoresistive sensor with microsphere surface and internal porous structure were prepared using a facile glass template method combining with supercritical carbon dioxide (Sc-CO2) foaming process. Effects of surface microstructure and cell structure on the piezoresistive sensing performance of TPU/MWCNT nanocomposite foam had been investigated in detail: the novel hybrid structure significantly improved the sensing performance with a fast response time (≤100 ms), a wide operating range (0 ∼ 80 kPa) and a stability of over 1000 cycles. In addition, the prepared piezoresistive sensor can be used to monitor a wide range of human physiological signals (facial activity, small arm muscle contraction and arterial pulse, etc.) due to its excellent integrated sensing performance. This work provides a route for the design of advanced piezoresistive sensors that show great potential for applications in human motion detection, artificial intelligence devices, and medical detection, etc.

Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey.

Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using internet of medical things (IoMT) devices. However, due to the centralized training approach of artificial intelligence (AI), mobile and wearable IoMT devices raise privacy issues concerning the information communicated between hospitals and end-users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm, has opened up new opportunities for privacy preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to end-users as only gradients are shared during training. For these specific properties of FL, in this paper, we present privacy-related issues in IoMT. Afterwards, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures by incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Moreover, we present some practical opportunities for FL in IoMT. In the end, we conclude this survey by discussing open research issues and challenges while using FL in future smart healthcare systems.

Improved resistive switching performance and in-depth mechanism analysis in Mn-doped SrTiO3-based RRAM

Since memristors have shown great application prospects in non-volatile memory, neural synapse, brain-like chips, artificial intelligence (AI) and quantum computers, the research of memristors has received extensive attention. In this work, the resistive switching (RS) device with Ag/SrTiO3 (STO)/Ti structure was fabricated, and it can significantly improve the RS performance of device by doping Mn ions into STO to replace part of Ti ions. That is to say, the Ag/Mn-doped SrTiO3 (SMTO)/Ti device exhibits larger resistance window and better retention at room temperature. Finally, through in-depth mechanistic analysis, a physical model of the conducting filament and Schottky emission was established to understand the charge transport mechanism and RS behavior of the device. In particular, it provides favorable factors for the migration of ions and electrons through the Mn doping because the substitution of Mn for Ti ions reduces the volume of the STO crystal while distorting its crystal shape since the ionic radius of Mn is smaller than that of Ti. This work provides an important way to improve the RS properties of ABO3-type perovskite-based resistive random access memory (RRAM), and helps to explore the cutting-edge applications of high-performance RS devices in information processing and AI.

Research progress and application of artificial intelligence in thyroid associated ophthalmopathy.

Thyroid-associated ophthalmopathy (TAO) is a complicated orbitopathy related to dysthyroid, which severely destroys the facial appearance and life quality without medical interference. The diagnosis and management of thyroid-associated ophthalmopathy are extremely intricate, as the number of professional ophthalmologists is limited and inadequate compared with the number of patients. Nowadays, medical applications based on artificial intelligence (AI) algorithms have been developed, which have proved effective in screening many chronic eye diseases. The advanced characteristics of automated artificial intelligence devices, such as rapidity, portability, and multi-platform compatibility, have led to significant progress in the early diagnosis and elaborate evaluation of these diseases in clinic. This study aimed to provide an overview of recent artificial intelligence applications in clinical diagnosis, activity and severity grading, and prediction of therapeutic outcomes in thyroid-associated ophthalmopathy. It also discussed the current challenges and future prospects of the development of artificial intelligence applications in treating thyroid-associated ophthalmopathy.

Major evolutionary transitions in individuality between humans and AI.

That humans might undergo future evolutionary transitions in individuality (ETIs) seems fanciful. However, drawing upon recent thinking concerning the origins of properties that underpin ETIs, I argue that certain ETIs are imminently realizable. Central to my argument is recognition that heritable variance in fitness at higher levels of organization can be externally imposed (scaffolded) by specific ecological structures and cultural practices. While ETIs to eusociality seem highly improbable, ETIs involving symbioses between humans and artificial intelligence (AI) can be readily envisaged. A necessary requirement is that fitness-affecting interactions between humans and AI devices are inherited by offspring. The Mendelian nature of human reproduction ensures that offspring resemble parents. Reproduction of AI devices requires nothing more than transference of algorithms from parental AI devices to devices that are assigned to offspring. This simple copying, combined with societal structures that require humans to carry AI devices, ensures heritable variance in fitness at the level of both interacting partners. Selection at the collective level will drive alignment of replicative fates and increase co-dependency, thus alleviating need for continual imposition of externally imposed scaffolds. I conclude by drawing attention to the immediacy of such transitions and express concern over possibilities for malevolent manipulation. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

Approval and Certification of Ophthalmic AI Devices in the European Union.

Artificial intelligence (AI)-based medical devices are already commercially available in Europe. The regulations surrounding the introduction and use of medical AI devices in the European Union (EU) are different to those in the USA, and the specifics of European legislature in medical AI are not commonly known. European law classifies medical devices into four classes: I, IIa, IIb, and III, depending on the perceived risk level of the device. Medical devices are certified under independent nongovernment bodies, and some can even self-certify their compliance with EU standards. The European "open" approach is vastly different from the strict perspective of the FDA, as reflected by the number of available medical AI devices. The EU is currently in a transitory period between two regulations, further complicating the legislative landscape. The devices in question deal with extremely sensitive data, collecting, processing, and sending images and diagnoses over the internet. The EU approach puts a large burden of verifying the effectiveness and integrity of the AI device on the consumer, without giving consumers many tools to do that effectively. This highlights the need for effective legislation and oversight from governing bodies, as well as the need for understanding the legalities and limitations of AI devices for those implementing them in clinical practice.

Addressing metastatic individuals everyday: Rationale and design of the nurse AMIE for Amazon Echo Show trial among metastatic breast cancer patients

BackgroundMetastatic Breast Cancer (MBC) patients often feel their symptom-related needs are unmet, despite visiting their doctors up to once a week. Novel approaches are needed to address symptoms without requiring additional appointments. Technology based symptom management approaches to address symptoms have not been well tested. MethodsNurse AMIE (Addressing Metastatic Individuals Everyday) is a technology based supportive care platform that provides guideline-concordant symptom management interventions in response to patient reported symptoms. We have previously successfully implemented a tablet version of Nurse AMIE. However, some eligible patients chose not to participate because they were overwhelmed by the technology. To address this barrier, we translated the Nurse AMIE platform to the Amazon Echo Show, which allowed for voice-based interactions. Forty-two MBC patients were randomized 1:1 to receive the Nurse AMIE for Echo Show immediately for six months, or to receive the same intervention for three months, after a three month delay. The primary outcome was change in physical distress over three months, and secondary outcomes included feasibility, acceptability, patient reported outcomes and usability. ConclusionsResults from the Nurse AMIE for Echo Show trial will identify the feasibility, acceptability, and preliminary effects of the Nurse AMIE for Echo Show on patient reported outcomes. Untested novel technologies, particularly voice-based artificial intelligence devices may an effective and scalable vehicle through which we can deliver supportive care interventions. Clinicaltrials.gov identifierNCT04673019.