AI Service Research Articles

Performance evaluation of the mammography ai services

Backgraund: Digital mammography is a key tool for diagnosing breast cancer (BC), which can reduce mortality by 20-40% due to early detection of pathology. To date, there have been developed many AI services that allow you to automate the process of evaluating such studies. Aims: The purpose of this work is to compare the results of different types and versions of AI services in relation to physician assessments in the analysis of mammography studies. Materials and methods: We have compared several binary scales for assessing mammography examinations, as well as several types and versions of AI services using accuracy metrics, the Matthews coefficient, and the maximum Youden index. Results: As a result of a comparative analysis, it was shown that the number of detected pathologies and the accuracy of assessing AI services depends on the binary scale for evaluating of the digital mammography. In addition, the maximum Youden index allows us to estimate the statistically significant difference between services and scales. According to this metric, AI service 1 and its version 3 have the best performance, which is consistent with most accuracy metrics. Conclusions: These results can be used in any field of medicine during the process of choosing AI services for clinical deployment.

The service trade with AI and energy efficiency: Multiplier effect of the digital economy in a green city by using quantum computation based on QUBO modeling

This research examines the energy efficiency of city districts through the Malmquist–DEA model and investigates the spatial effects of the service trade and the digital economy on energy efficiency in urban green development. The study also delves into the specific context of the AI service trade to gain insights into and align with the emerging digital intelligence industry. The interplay of the service trade with the digital economy, alongside the AI service trade with innovation, significantly enhances urban energy efficiency and demonstrates positive externalities. Building on the empirical findings, this research employs cluster analysis to explore the green development of city districts and uses AI technology to program green communication and cooperation mechanisms across district clusters, employing quantum computation based on QUBO modeling. This study contributes to a deeper understanding of the cofunction of the service trade and the digital economy in terms of energy efficiency and aids in developing new quality productivities for green cities through quantum-based AI advancements. This research has clear implications for cutting-edge interdisciplinary applications of green AI technologies in social computing science.

Adaptive reverse task offloading in edge computing for AI processes

Nowadays, we witness the proliferation of edge IoT devices, ranging from smart cameras to autonomous vehicles, with increasing computing capabilities, used to implement AI-based services in users’ proximity, right at the edge. As these services are often computationally demanding, the popular paradigm of offloading their tasks to nearby cloud servers has gained much traction and been studied extensively. In this work, we propose a new paradigm that departs from the above typical edge computing offloading idea. Namely, we argue that it is possible to leverage these end nodes to assist larger nodes (e.g., cloudlets) in executing AI tasks. Indeed, as more and more end nodes are deployed, they create an abundance of idle computing capacity, which, when aggregated and exploited in a systematic fashion, can be proved beneficial. We introduce the idea of reverse offloading and study a scenario where a powerful node splits an AI task into a group of subtasks and assigns them to a set of nearby edge IoT nodes. The goal of each node is to minimize the overall execution time, which is constrained by the slowest subtask, while adhering to predetermined energy consumption and AI performance constraints. This is a challenging MINLP (Mixed Integer Non-Linear Problem) optimization problem that we tackle with a novel approach through our newly introduced EAI-ARO (Edge AI-Adaptive Reverse Offloading) algorithm. Furthermore, a demonstration of the efficacy of our reverse offloading proposal using an edge computing testbed and a representative AI service is performed. The findings suggest that our method optimizes the system’s performance significantly when compared with a greedy and a baseline task offloading algorithm.

Beyond Automation: How AI Service Integration is Shaping the Future of Brand Management

Beyond Automation: How AI Service Integration is Shaping the Future of Brand Management

Milk Value Chain Analysis: The Case of Urban and Peri-Urban Areas of Ambo and Dendi Districts, West Shewa Zone, Oromia National Regional State, Ethiopia

This study was aimed at analyzing value chain of milk in Urban and Peri – Urban areas of Ambo and Dendi districts of West Shewa Zone. The study made use of both primary and secondary data collected from 146 smallholder farmers, 30 consumers, 10 Hotels and Cafeterias and 2 primary milk cooperatives through application of individual interview schedules, focus group discussions and key informant interview. The analysis was made using descriptive statistics and value chain analysis. The value chain analysis revealed that the major actors in the districts milk value chains are input suppliers, milk producers, collectors, processors and consumers. Accordingly, the value chain activities in the chain were input supply, production, processing, marketing and consumption. Four milk marketing channels were identified and relative to all other fresh milk marketing channels, producers get a higher proportion of total gross marketing margin when they sell milk to cooperatives. Profitability analysis result for dairy production indicated that, a dairy producer obtains average gross margin of 83.65per improved cow per day, in the study areas which shows that dairy production was a profitable venture for smallholder dairy producers. Milk value chain in the study areas was hindered by shortage of feeds, continuous increasing price of feeds, poor AI service provision, poor supply of genetically improved cattle breeds, limited availability of credit services, limited extension service provision, poor feeding and animal husbandry practices, low level of market linkage among the value chain actors, absence of milk processing plant and non-market oriented milk production. Therefore, policy aiming at increasing farmers’ access to modern inputs, feed conservation practices, adequate training provision to AI technicians, developing and improving infrastructure, cooperative development and improving extension services are recommended to accelerate the milk value chain’s development in the study areas.

Digital Risk Management: A Study of How Firms Mitigate Digital Risks and Threats

The banking industry has seen a sharp rise in the use of AI services over the last ten years, which has improved client satisfaction and efficiency. The machine learning (ML), one of artificial intelligence's primary building blocks, has caused a paradigm change in the banking services sector. Every new technology that is introduced carries with it new threats. Finding such hazards is only the first step; you also need to analyse, evaluate, treat, regulate, and keep an eye on them. Risk is characterised by three elements: the possibility of loss, the unpredictability of the result, and the necessity of making a decision. According to this concept, a risk management strategy should be divided into three stages to address risks: planning ahead, carrying out risk management tasks, and maintaining and enhancing risk management. The first two stages could appear self-evident. But it's critical to recognise that risk management is a continuous process that requires constant attention. Continuous risk management is intended to avert issues, enhance product quality, make better use of available resources, and foster teamwork.

Are companies better off with AI? The effect of AI service failure events on firm value

PurposeAs more firms adopted AI-related services in recent years, AI service failures have increased. However, the potential costs of AI implementation are not well understood, especially the effect of AI service failure events. This study examines the influences of AI service failure events, including their industry, size, timing, and type, on firm value.Design/methodology/approachThis study will conduct an event study of 120 AI service failure events in listed companies to evaluate the costs of such events.FindingsFirst, AI service failure events have a negative impact on the firm value. Second, small firms experience more share price declines due to AI service failure events than large firms. Third, AI service failure events in more recent years have a more intensively negative impact than those in more distant years. Finally, we identify different types of AI service failure and find that there are order effects on firm value across the service failure event types: accuracy > safety > privacy > fairness.Originality/valueFirst, this study is the initial effort to empirically examine market reactions to AI service failure events using the event study method. Second, this study comprehensively considers the effect of contextual influencing factors, including industry type, firm size and event year. Third, this study improves the understanding of AI service failure by proposing a novel classification and disclosing the detailed impacts of different event types, which provides valuable guidance for managers and developers.

Application of large language model combined with retrieval enhanced generation technology in digestive endoscopic nursing.

Although large language models (LLMs) have demonstrated powerful capabilities in general domains, they may output information in the medical field that could be incorrect, incomplete, or fabricated. They are also unable to answer personalized questions related to departments or individual patient health. Retrieval-augmented generation technology (RAG) can introduce external knowledge bases and utilize the retrieved information to generate answers or text, thereby enhancing prediction accuracy. We introduced internal departmental data and 17 commonly used gastroenterology guidelines as a knowledge base. Based on RAG, we developed the Endo-chat medical chat application, which can answer patient questions related to gastrointestinal endoscopy. We then included 200 patients undergoing gastrointestinal endoscopy, randomly divided into two groups of 100 each, for a questionnaire survey. A comparative evaluation was conducted between the traditional manual methods and Endo-chat. Compared to ChatGPT, Endo-chat can accurately and professionally answer relevant questions after matching the knowledge base. In terms of response efficiency, completeness, and patient satisfaction, Endo-chat outperformed manual methods significantly. There was no statistical difference in response accuracy between the two. Patients showed a preference for AI services and expressed support for the introduction of AI. All participating nurses in the survey believed that introducing AI could reduce nursing workload. In clinical practice, Endo-chat can be used as a highly effective auxiliary tool for digestive endoscopic care.

The Analysis of Factors Affecting College Students’ Use of AI Services

The Analysis of Factors Affecting College Students’ Use of AI Services

Intentional or Designed? The Impact of Stance Attribution on Cognitive Processing of Generative AI Service Failures.

With the rapid expansion of the generative AI market, conducting in-depth research on cognitive conflicts in human-computer interaction is crucial for optimizing user experience and improving the quality of interactions with AI systems. However, existing studies insufficiently explore the role of user cognitive conflicts and the explanation of stance attribution in the design of human-computer interactions. This research, grounded in mental models theory and employing an improved version of the oddball paradigm, utilizes Event-Related Spectral Perturbations (ERSP) and functional connectivity analysis to reveal how task types and stance attribution explanations in generative AI influence users' unconscious cognitive processing mechanisms during service failures. The results indicate that under design stance explanations, the ERSP and Phase Locking Value (PLV) in the theta frequency band were significantly lower for emotional task failures than mechanical task failures. In the case of emotional task failures, the ERSP and PLV in the theta frequency band induced by intentional stance explanations were significantly higher than those induced by design stance explanations. This study found that stance attribution explanations profoundly affect users' mental models of AI, which determine their responses to service failure.

Responsible Crowdsourcing for Responsible Generative AI: Engaging Crowds in AI Auditing and Evaluation

With the rise of generative AI (GenAI), there has been an increased need for participation by large and diverse user bases in AI evaluation and auditing. GenAI developers are increasingly adopting crowdsourcing approaches to test and audit their AI products and services. However, it remains an open question how to design and deploy responsible and effective crowdsourcing pipelines for AI auditing and evaluation. This workshop aims to take a step towards bridging this gap. Our interdisciplinary team of organizers will work with workshop participants to explore several key questions, such as how to improve the output quality and workers' productivity for GenAI evaluation crowdsourcing tasks compared to discriminative AI systems, how to guide crowds in auditing problematic AI-generated content while managing their psychological impact, ensuring marginalized voices are heard, and setting up responsible and effective crowdsourcing pipelines for real-world GenAI evaluation. We hope this workshop will produce a research agenda and best practices for designing responsible crowd-based approaches to AI auditing and evaluation.

Enhancing ophthalmology medical record management with multi-modal knowledge graphs

The electronic medical record management system plays a crucial role in clinical practice, optimizing the recording and management of healthcare data. To enhance the functionality of the medical record management system, this paper develops a customized schema designed for ophthalmic diseases. A multi-modal knowledge graph is constructed, which is built upon expert-reviewed and de-identified real-world ophthalmology medical data. Based on this data, we propose an auxiliary diagnostic model based on a contrastive graph attention network (CGAT-ADM), which uses the patient’s diagnostic results as anchor points and achieves auxiliary medical record diagnosis services through graph clustering. By implementing contrastive methods and feature fusion of node types, text, and numerical information in medical records, the CGAT-ADM model achieved an average precision of 0.8563 for the top 20 similar case retrievals, indicating high performance in identifying analogous diagnoses. Our research findings suggest that medical record management systems underpinned by multimodal knowledge graphs significantly enhance the development of AI services. These systems offer a range of benefits, from facilitating assisted diagnosis and addressing similar patient inquiries to delving into potential case connections and disease patterns. This comprehensive approach empowers healthcare professionals to garner deeper insights and make well-informed decisions.

Explanation of time perspectives in adopting AI service robots under different service settings

This study bridges a gap in AI acceptance literature by integrating Time Perspective Theory with the Technology Acceptance Model by examining how time orientations influence AI acceptance in various service settings. The results show that past-positive, present-hedonistic, and future time perspectives impact individuals' privacy concerns and their recognition of AI's utilitarian and hedonic benefits. Conversely, past-negative and present-fatalistic perspectives show negligible effects. The study highlights distinct patterns in credence versus experience services, with future-oriented and present-hedonistic individuals favoring AI's benefits in hospitals over restaurants, and past-positive individuals valuing hedonic benefits more in restaurant settings. These orientations affect perceived usefulness and ease of use, with privacy concerns significantly influencing ease of use. The findings offer significant theoretical and practical implications, underscoring the nuanced role of time perspectives in AI service robot acceptance across different environments.

Delaying induction of ovulation and timed AI in a Double-Ovsynch protocol increased expression of estrus and altered first service reproductive outcomes of lactating dairy cows

The objective of this randomized controlled experiment was to evaluate the effect of delaying induction of ovulation and timed artificial insemination (TAI) on expression of estrus before AI and first service reproductive outcomes. A secondary objective was to evaluate the effects of delaying induction of ovulation in a Double-Ovsynch protocol on ovarian function. Lactating Holstein cows (n = 4,672) from 2 commercial dairy farms fitted with sensors for automated detection of estrus were synchronized with a Double-Ovsynch protocol up to the first PGF2α (PGF-L) of the Breeding-Ovsynch portion of the protocol (Pre-Ovsynch: GnRH, 7 d later PGF2α, 3 d later GnRH, 7 d later Breeding-Ovsynch: GnRH, 7 d later PGF2α, 1 d later PGF2α). At PGF-L, cows blocked by parity (primiparous vs. multiparous) and semen used for first service (sex-sorted dairy vs. conventional beef) were randomly assigned to the G56 (n = 2,338) or G80 (n = 2,334) treatments. Cows in G56 had 56 h whereas cows in G80 had 80 h from PGF-L to induction of ovulation with the last GnRH (GnRH2) before AI. For both treatments, TAI occurred ∼16 h after GnRH2. All cows with automated estrus alerts between PGF-L and TAI were inseminated at detected estrus (AIE) without GnRH. Ovarian function and responses to synchronization were monitored based on circulating concentrations of progesterone and examination of the ovaries by ultrasonography. Data for binary outcomes were analyzed by logistic and continuous outcomes with lineal regression. More cows in G80 received AIE and had estrus before AI. Overall, pregnancies per AI (P/AI) did not differ for the G80 and G56 treatments. Cows in G80 that received TAI and had no estrus had fewer P/AI than cows with estrus that received AIE or TAI in G80, and fewer P/AI than cows AIE and cows that received TAI and had or did not have estrus in the G56 treatment. No differences were observed between treatments or for cows with and without estrus for pregnancy loss. Unlike some minor differences between treatments for concentrations of progesterone at GnRH2, the most notable differences in ovarian function were for cows in both treatments with or without estrus that received TAI. Cows with estrus, were more likely to have follicles > 16 mm, had larger follicles before ovulation, and had a greater ovulation risk after AI. Likewise, within the G80 treatment only, cows with estrus that received AIE or TAI had larger follicles, were more likely to have complete luteal regression, had greater ovulation risk, were more likely to have a functional corpus luteum, and had more circulating progesterone after AI. We concluded that delaying induction of ovulation and TAI was effective for allowing more cows to express estrus before AI which had different ovarian function outcomes and greater P/AI than cows that did not express estrus. However, the greater P/AI of cows that expressed estrus was insufficient to compensate for the reduced P/AI of cows that did not express estrus, and thus increase overall P/AI compared with the treatment without delayed induction of ovulation. Detection of estrus before AI in cows undergoing synchronization of ovulation could help identify cows with different likelihoods of pregnancy after insemination.

What drives AI-based risk information-seeking intent? Insufficiency of risk information versus (Un)certainty of AI chatbots

This study explored the factors influencing the U.S. public's intent to seek risk information via AI-powered channels, such as ChatGPT. It focused on cognitive and affective pathways that lead to uncertainty about both risk information and AI chatbots in the context of climate change risk. We conducted a comparative analysis to discern the impacts of risk perceptions related to climate change and AI-caused privacy risks on public uncertainty and decision-making regarding the use of AI chatbots. Specifically, we assessed how different risk-related perceptions and emotions contribute to subsequent uncertainty perceptions and decision-making regarding AI chatbot use for climate change risk information. We enlisted 1023 U.S. citizens aged 21–65 via CloudResearch in September 2023. The results reveal that high levels of perceived risk, strong negative emotions, and information insufficiency drive information-seeking behavior through AI chatbots. Perceived privacy concerns about AI technology significantly increase AI anxiety, which is positively associated with perceived uncertainty. Both AI anxiety and perceived uncertainty negatively affect the intent to seek information via AI chatbots. Conversely, perceived trust in AI chatbots significantly increases positive emotional responses, reduces perceived uncertainty, and enhances the intent to seek information via AI chatbots. We also investigated the mediation effects within each study model tested. The findings offer theoretical and practical implications for future studies on the public's adoption of AI services for risk information seeking, influenced by both risk-related and technology-based contexts.

A Study on Customized Education AI Services for Employment Support of Individuals with Borderline Intellectual Functioning

A Study on Customized Education AI Services for Employment Support of Individuals with Borderline Intellectual Functioning

A Study on the Characteristics of Image-Generative AI Services as Design Tools and Applicability in Graphic Design Education

A Study on the Characteristics of Image-Generative AI Services as Design Tools and Applicability in Graphic Design Education

중학교 가정교과 인공지능 활용 식생활 교수⋅학습 과정안 개발 및 적용

The purpose of this study was to design a lesson utilizing artificial intelligence in the dietary area of home economics and to investigate the effectiveness of the lesson by implementing it. To achieve the purpose of the study, practical problem-based lessons were selected by analyzing the achievement standards of the home economics curriculum, and strategies using AI were designed and put into the process of practical problem-based lessons to develop a teaching and learning curriculum. This was implemented on middle school students to assess the impact on the three systems of action and to evaluate strengths and areas for improvement. The learning topic is set as “What dietary choices should we make to lead a healthy and self-directed life?” and developed a teaching and learning plan, learning activities, and rubrics for four sessions utilizing generative AI services such as ChatGPT, WRTN, Askup, and typecast. The developed teaching and learning plan was implemented to 170 first-grade students middle school in Gwangju. A paired sample t-test was conducted on the pre- and post-test of the three systems of action, and it was found that there was a significant difference between the pre- and post-test (t=-2.86, p<.05). Students gave positive feedback on the AI-assisted dietary class, as it provided diverse and accessible information for analyzing dietary problems and identifying causes and solutions, and enabled efficient completion of learning tasks.

Influence of robot anthropomorphism on consumer attitudes toward restaurants and service providers

We conducted this study to investigate the influence of robot anthropomorphism on consumers’ attitudes toward restaurants and service providers, focusing on the moderating effects of robot social roles and consumer autonomy. The results of Study 1 revealed that anthropomorphic robots fostered more positive attitudes toward restaurants and service robots than non-anthropomorphic robots when consumers viewed restaurant advertisements. The results of Study 2 revealed that consumers also exhibited more positive attitudes toward anthropomorphic robots compared to non-anthropomorphic robots when picturing restaurant dining experiences. Furthermore, when non-anthropomorphic robots were assigned partner roles and consumers were given the option to choose their servers, attitudes toward these robots improved. Collectively, these findings suggest that anthropomorphism in robots can lead to more positive attitudes, and the acceptance of non-anthropomorphic robots can be enhanced through greater consumer autonomy and the assignment of partner roles. These results provide insight into AI service marketing strategies.

Effects of customer inoculation on artificial intelligence service failure

Purpose This paper aims to explore the effectiveness of customer inoculation strategies in the context of AI service failures in the hospitality and tourism industries. Furthermore, it examines how these strategies can enhance customer complaint behavior and satisfaction with service recovery, thereby improving the overall service experience. Design/methodology/approach Four distinct studies were conducted: Study 1 investigated the influence of customer inoculation on complaint behavior post-AI service failure. Study 2 assessed the impact of service remedies on customer satisfaction. Study 3 explored the implications of initial purchase and usage intentions. Finally, Study 4 validated the findings using a large-scale online survey. Findings The results indicated that customer inoculation significantly increases customer complaint behavior and satisfaction with service remedies following AI service failures. They also showed that this relationship is mediated by psychological distance. Furthermore, customer inoculation positively affects initial purchase and usage intentions, demonstrating effectiveness at various customer engagement stages. Practical implications This study enriches the literature on AI hospitality service failure and recovery by introducing the novel concept of customer inoculation. Additionally, it significantly contributes to the inoculation theory literature, which covers diverse fields. Practically, this study proposes an efficient and low-cost strategy for marketers. Originality/value This study introduces the concept of customer inoculation in the context of AI service failures, a novel approach in the hospitality and tourism literature. It provides empirical evidence of the efficacy of the strategy, bridging a crucial gap in understanding customer behavior in the face of technological disruptions.