Virtual Assistant Research Articles

Staffing Shortages? Time to Consider a Virtual Assistant

We are experiencing declining reimbursements and rising costly overheads in American healthcare. The modern healthcare provider faces the challenge of balancing efficient healthcare with the overload of paperwork that is increasing burnout and motivating doctors to early retirement. Using virtual assistants as a staffing alternative addresses the needs of many medical practices, regardless of their size or location. This article explores the role of virtual assistants and their ability to revolutionize healthcare delivery and offers information on implementing a virtual assistant.

Ethel: A virtual teaching assistant

Ethel: A virtual teaching assistant

Privacy, confidentiality and ethical concerns in audio ai assistants: A comparative study of North American, European, and Asian Markets

This study examines the ethical, privacy, and confidentiality issues with audio AI assistants in the markets of North America, Europe, and Asia. Understanding the ramifications of data collecting, storage, and usage is crucial as the use of technologies like Google Assistant and Amazon Alexa grows. The study highlights significant regulatory differences between the areas; North America offers a fragmented landscape, and many Asian nations are still developing their data protection legislation, while Europe leads with strong frameworks like the General Data Protection Regulation (GDPR). Key challenges identified include a lack of user awareness regarding privacy rights, extensive data collection practices, unauthorized listening, data breaches, and cultural differences influencing attitudes toward data protection. The findings reveal that these challenges impact user trust and hinder the responsible innovation of AI technologies. Recommendations for improvement include establishing global regulations to promote consistency in data protection, adopting best practices such as privacy-by-design principles, and enhancing user education initiatives. Additionally, the study identifies critical areas for future research, particularly the role of AI assistants in sensitive sectors like healthcare and education, where privacy concerns may differ significantly. Overall, this comparative analysis provides a foundational understanding of audio AI assistants' ethical landscape and underscores the importance of addressing these issues to foster a secure and trustworthy digital environment.

The Role of Artificial Intelligence (AI) and machine learning in social work practice

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into social work practice is transforming the landscape of service delivery and decision-making. This paper explores how these technologies enhance case management, predictive analytics, and resource allocation in critical areas such as child welfare, mental health, and substance abuse treatment. Key trends highlighted include the use of AI-based predictive analytics to identify at-risk populations and facilitate early interventions, as well as the deployment of chatbots and virtual assistants for providing accessible mental health counselling and social support. Furthermore, the paper addresses ethical considerations and challenges associated with AI implementation, particularly the potential biases in algorithms that may affect the assessment of social needs. Additionally, the integration of AI tools into social work education and training is examined to prepare future professionals for a technology-driven environment. By analysing the current applications of Natural Language Processing (NLP) for client data analysis, AI-powered software for predictive risk assessments, and automated case management systems, this paper advocates for a balanced approach to AI adoption, ensuring that the core values of social work, such as equity and social justice, remain central to practice. Ultimately, this exploration underscores the potential of AI and ML to enhance social work outcomes while also emphasizing the necessity of ethical frameworks and ongoing training for practitioners.

Close clinical monitoring of patients after TAVI implantation using artificial intelligence with a virtual voice assistant

Abstract Introduction The number of procedures and patients candidates for transcatheter aortic valve implantation (TAVI) has increased considerably due to population ageing and the rise in scientific evidence supporting this technique in patients with increasingly lower surgical risk. Early discharge would allow for a more efficient use of available resources, in addition to improving patients’ functionality and quality of life. Objective The use of a virtual voice assistant based on artificial intelligence, could be an effective and safe alternative to closely monitor patients after TAVI implantation in order to facilitate early identification of complications and a more efficient use of the resources available in Cardiology services. Material and methods Observational, prospective, single-center and consecutive study of the implementation of a technology based on artificial intelligence and natural language processing to monitor all patients undergoing transfemoral TAVI implantation, according to usual clinical practice, in the hemodynamics unit of our hospital in the year 2023. The virtual voice assistant, "Lola", carries out a telephonic follow-up during week 1, week 2, month 1, month 3 and 12. In these calls, it asks a series of questions mainly related to vascular access and with the patient's cardiovascular situation. After finishing the call, all the information collected is uploaded to a web platform where we monitor the collected data and thus take the appropriate measures. Results After 12 months, 274 patients have been included to whom 1,039 calls have been made with a total of 385 hours of autonomous conversation. In 44% of the calls, no alerts were detected and, therefore, did not required further review. From the remaining calls, a total of 926 alerts were collected, the predominant ones being: dizziness (24.9%), contact with the emergency services (11.3%) and alteration of general status (10.4%). The number of alerts decreases as the follow-up progresses, reflecting the need for closer monitoring in the initial stages after implantation. These alerts generated at least one intervention in 57% of the calls, the predominant ones being: medical contact (43.0%), nursing contact (26.3%) and medication adjustment (4.0%). These calls resulted in a total of 15 in-person examinations (5.4% of patients), detecting in 2 of them a 3rd degree atrioventricular block, in 3 a femoral pseudoaneurysm and in 1 a critical stenosis of the common femoral artery. According to a patient survey, 88.9% of patients were satisfied or very satisfied (Customer Satisfaction score 4.68/5). Conclusions The application of artificial intelligence permits strict control of the patient after TAVI implantation, thus facilitating early detection of complications. Patient satisfaction regarding this follow-up system was very high.

“Your friendly AI assistant”: the anthropomorphic self-representations of ChatGPT and its implications for imagining AI

AbstractThis study analyzes how ChatGPT portrays and describes itself, revealing misleading myths about AI technologies, specifically conversational agents based on large language models. This analysis allows for critical reflection on the potential harm these misconceptions may pose for public understanding of AI and related technologies. While previous research has explored AI discourses and representations more generally, few studies focus specifically on AI chatbots. To narrow this research gap, an experimental-qualitative investigation into auto-generated AI representations based on prompting was conducted. Over the course of a month, ChatGPT (both in its GPT-4 and GPT-4o models) was prompted to “Draw an image of yourself,” “Represent yourself visually,” and “Envision yourself visually.” The resulting data (n = 50 images and 58 texts) was subjected to a critical exploratory visual semiotic analysis to identify recurring themes and tendencies in how ChatGPT is represented and characterized. Three themes emerged from the analysis: anthropomorphism, futuristic/futurism and (social)intelligence. Importantly, compared to broader AI imaginations, the findings emphasize ChatGPT as a friendly AI assistant. These results raise critical questions about trust in these systems, not only in terms of their capability to produce reliable information and handle personal data, but also in terms of human–computer relations.

Toward Process-Oriented, Dimensional Approaches for Diagnosis and Treatment of Speech Sound Disorders in Children: Position Statement and Future Perspectives.

Children with speech sound disorders (SSD) form a heterogeneous group, with respect to severity, etiology, proximal causes, speech error characteristics, and response to treatment. Infants develop speech and language in interaction with neurological maturation and general perceptual, motoric, and cognitive skills in a social-emotional context. After a brief introduction into psycholinguistic models of speech production and levels of causation, in this review article, we present an in-depth overview of mechanisms and processes, and the dynamics thereof, which are crucial in typical speech development. These basic mechanisms and processes are: (a) neurophysiological motor refinement, that is, the maturational articulatory mechanisms that drive babbling and the more differentiated production of larger speech patterns; (b) sensorimotor integration, which forms the steering function from phonetics to phonology; and (c) motor hierarchy and articulatory phonology describing the gestural organization of syllables, which underlie fluent speech production. These dynamics have consequences for the diagnosis and further analysis of SSD in children. We argue that current diagnostic classification systems do not do justice to the multilevel, multifactorial, and interactive character of the underlying mechanisms and processes. This is illustrated by a recent Dutch study yielding distinct performance profiles among children with SSD, which allows for a dimensional interpretation of underlying processing deficits. Analyses of mainstream treatments with respect to the treatment goals and the speech mechanisms addressed show that treatment programs are quite transparent in their aims and approach and how they contribute to remediating specific deficits or mechanisms. Recent studies into clinical reasoning reveal that the clinical challenge for speech-language pathologists is how to select the most appropriate treatment at the most appropriate time for each individual child with SSD. We argue that a process-oriented approach has merits as compared to categorical diagnostics as a toolbox to aid in the interpretation of the speech profile in terms of underlying deficits and to connect these to a specific intervention approach and treatment target.

Leveraging AI-Driven financial literacy and tax compliance solutions to foster economic growth in underserved communities and small businesses

Underserved communities and small businesses face significant challenges in financial literacy and tax compliance, both of which are critical to economic stability and growth. Limited access to financial education and the complexities of tax systems create barriers that hinder these groups from fully participating in the formal economy. This review explores the potential of leveraging artificial intelligence (AI) to address these challenges by providing innovative solutions that enhance financial literacy and simplify tax compliance. AI-driven platforms can offer personalized financial education, using machine learning algorithms to identify knowledge gaps and provide tailored resources to users. Through gamification and interactive tools, AI can make financial education more engaging and accessible, especially for individuals with limited formal education. Furthermore, AI-powered virtual assistants and chatbots can guide users through essential financial decisions, improving budgeting, credit management, and savings habits. In the realm of tax compliance, AI solutions can automate bookkeeping, tax filings, and other administrative tasks, reducing human error and lowering the compliance burden on small businesses. Predictive analytics can assist in tax planning by forecasting liabilities and helping businesses make informed decisions that minimize tax penalties and maximize cash flow. These advancements empower small businesses and entrepreneurs by enabling better financial management, fostering trust in the financial system, and facilitating access to funding opportunities. By integrating AI-driven financial literacy and tax compliance solutions, underserved communities and small businesses can unlock their economic potential, contributing to broader economic growth and financial inclusion. However, challenges such as data privacy, digital access, and regulatory concerns must be addressed to ensure equitable implementation of these technologies. The review concludes that with careful consideration of these factors, AI can be a powerful catalyst for sustainable economic development. Keywords: Leveraging AI-Driven, Financial Literacy Tax Compliance Small Businesses

Transforming Libraries: The Impact of Artificial Intelligence

Artificial intelligence (AI) is revolutionising library services and enhancing user experience, creating a new era of efficiency, accessibility, and innovation. This editorial explores how AI reshapes the library landscape, ushering in a new generation of efficiency, accessibility, and innovation. The AI’s most notable contribution to libraries is the automation of routine tasks, cataloguing and organising; librarians can now rely on AI algorithms to streamline. It saves librarians valuable time engaging in intellectually stimulating activities and ensures a more accurate and organised library system. AI-driven systems can efficiently analyse vast amounts of data, leading to improved search functionalities and more seamless information retrieval for library patrons. Moreover, AI is instrumental in the personalisation of library services. Through machine learning algorithms, libraries can analyse user behaviour, preferences, and historical data to provide customised recommendations. This enhances the user experience by offering tailored suggestions for reading materials, resources, and services, making libraries more relevant and engaging for diverse user groups. Another essential role of AI in libraries is the development of virtual assistants. These digital assistants, powered by natural language processing and machine learning, can help library patrons navigate the catalogue system, answer queries, and even provide Realtime language translation service. Virtual assistants help libraries become more inclusive and accessible, reaching more audiences with diverse language backgrounds and information needs.

Application of Interactive Learning Methods and Character Strengthening in Aisyiyah Penatarsewu Kindergarten

This research aims to explore the role of Real Work Lecture (KKN) students in supporting learning activities at the Aisyiyah Kindergarten in Penatarsewu Village and their contribution in increasing awareness of children's dental health. Using a qualitative descriptive approach, this research assesses the impact of KKN on educational learning, social and cultural introduction, and children's health. The research results show that KKN students have succeeded in applying creative and innovative methods, introducing local social and cultural values, and increasing children's understanding of dental health, including the importance of limiting consumption of sweet foods. The involvement of KKN students makes a significant contribution to improving the quality of education, social knowledge, culture and children's health. This program shows the potential for synergy between higher education and local communities in creating sustainable positive change.

Artificial Intelligence to support Children with Autism

Children with autism spectrum disorder (ASD) face challenges in communication, social interaction, and behavioral flexibility, which can affect their development and everyday functioning. Artificial intelligence (AI) offers innovative tools to support children with autism by providing personalized and adaptive interventions. AI-powered systems, such as robots, virtual assistants, and machine learning models, can be used to enhance social skills training, improve communication, and manage sensory sensitivities. These technologies can monitor progress, adjust strategies, and provide real-time feedback, enabling more effective, individualized care. This paper explores the potential of AI in autism support, focusing on AI applications for social skill enhancement, early detection, and therapeutic intervention, while also addressing the ethical considerations and challenges involved in integrating AI into autism care.

Human-AI Collaboration in Cooperative Games: A Study of Playing Codenames with an LLM Assistant

Playing partial information, restricted communication, cooperative (PIRCC) games with humans have proven challenging for AI, due to our reliance on social dynamics and sophisticated cognitive techniques. Yet, recent advances in generative AI may change this situation through new forms of human-AI collaboration. This paper investigates how teams of players interact with an AI assistant in the PIRCC game Codenames and the impact this has on cognition, social dynamics, and player experience. We observed gameplay and conducted post-game focus groups with 54 participants across ten groups. Each group played three rounds of Codenames, with an AI assistant supporting Cluegivers. We found the AI assistant enhanced players' convergent and divergent thinking, but interfered with formation of team mental models, highlighting a tension in the use of AI in creative team scenarios. The presence of the AI challenged many players' understanding of the 'spirit of the game'. Furthermore, the presence of the AI assistants weakened social connections between human teammates, but strengthened connections across teams. This paper provides an empirical account of an AI assistant's effect on cognition, social dynamics, and player experience in Codenames. We highlight the opportunities and challenges that arise when designing hybrid digital boardgames that include AI assistants.

Utility-Oriented Knowledge Graph Accuracy Estimation with Limited Annotations: A Case Study on DBpedia

Knowledge Graphs (KGs) are essential for applications like search, recommendation, and virtual assistants, where their accuracy directly impacts effectiveness. However, due to their large-scale and ever-evolving nature, it is impractical to manually evaluate all KG contents. We propose a framework that employs sampling, estimation, and active learning to audit KG accuracy in a cost-effective manner. The framework prioritizes KG facts based on their utility to downstream tasks. We applied the framework to DBpedia and gathered annotations from both expert and layman annotators. We also explored the potential of Large Language Models (LLMs) as KG evaluators, showing that while they can perform comparably to low-quality human annotators, they tend to overestimate KG accuracy. As such, LLMs are currently insufficient to replace human crowdworkers in the evaluation process. The results also provide insights into the scalability of methods for auditing KGs.

Enhancing Intent Classifier Training with Large Language Model-generated Data

ABSTRACT Intent classification is essential in Natural Language Processing, serving applications like virtual assistants and customer service by categorizing user inputs into predefined classes. Despite its importance, the effectiveness of intent classifiers is often constrained by the scarcity of labeled data, as acquiring substantial, annotated datasets is costly and impractical. Data augmentation addresses this by expanding datasets with modified or synthetic examples, a common practice in computer vision but more complex in NLP due to the discrete nature of language. Traditional NLP data augmentation methods have been explored but exhibit limitations. This paper investigates the use of Large Language Models for generating labeled data to enhance intent classification. We explore whether LLM-generated data can effectively augment training sets, comparing its impact on intent classifier performance against traditional augmentation methods. Our study reveals that LLMs can generate diverse and realistic data, potentially improving classifier accuracy in low-data scenarios, thereby providing valuable insights into leveraging generative AI for NLP tasks in real-world applications.

Generative AI as a catalyst for HRM practices: mediating effects of trust

This study investigated the impact of generative AI tools on human resource management practices, organizational commitment, employee engagement, and employee performance. The authors also investigated the mediating role of trust in the relationship between user perception and organizational commitment. A structured questionnaire was used to collect the data from the information technology industry employees to measure the 9 reflective constructs optimism, innovativeness, ease of use, usefulness, trust, organizational commitment, vigor, dedication, and employee performance. Two constructs, ease of use and usefulness, are modeled as a higher-order construct – User Perception, whereas vigor and Dedication are modeled as another higher-order construct, Employee Engagement. The mode-fit indices were assessed for both the higher- and lower-order constructs, and the model-fit indices for both the models, higher- and lower-order constructs reveal an excellent model fit. Positive and statistically significant impacts were observed between the study constructs. The impact of organizational commitment on employee engagement was positive and statistically significant, and in turn, the impact of employee engagement on employee performance was also positive and statistically significant. Trust partially mediated the relationship between user perception and organizational commitment, fostering enhanced employee engagement and performance. The theories of technology readiness, stimulus-organization-response, and technology acceptance model were utilized in the development of the study’s theoretical framework, which provided fresh perspectives on how employee engagement, organizational commitment, and performance are influenced by user experience and trust in the context of generative AI. Theoretically, it opens up new applications such as personalized education and services, digital art, and realistic virtual assistants that were previously unfeasible or impractical for automation. The practical implications are that the field of interdisciplinary research as well as the information technology industry will be greatly impacted by generative AI. To facilitate swift adoption, the principles of generative AI are conceptualized from model-, system-, and application-level perspectives in addition to a social-technical perspective, where they are explicated and defined. In the end, our research has given future scholars a significant research agenda that will enable them to study generative AI from different theoretical perspectives while incorporating the concepts from these theories.

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ДИСТАНЦИОННОМ ОБУЧЕНИИ: НОВЫЕ ПОДХОДЫ И ИНСТРУМЕНТЫ

This article discusses new approaches and tools based on artificial intelligence that can be effectively used in teaching. In particular, the analysis will be carried out on adaptive learning, intelligent knowledge assessment systems, virtual assistants and chatbots, as well as the use of analytical data to predict student academic success. Particular attention is paid to how these technologies can improve the quality of the educational process, improve interaction between students and teachers, and provide a more personalized experience. The article also discusses the challenges and problems associated with the implementation of AI in the educational process, including data protection issues, ethical aspects of the use of AI, as well as issues of technology availability in different regions.

A virtual assistant for primary care's food and nutrition surveillance system: Development and validation study in Brazil

ObjectiveThe study aimed to develop and validate a conversational agent (chatbot) designed to support Food and Nutrition Surveillance (FNS) practices in primary health care settings. MethodsThis mixed-methods research was conducted in three stages. Initially, the study identified barriers and challenges in FNS practices through a literature review and feedback from 655 health professionals and FNS experts across Brazil. Following this, a participatory design approach was employed to develop and validate the chatbot's content. The final stage involved evaluating the chatbot's user experience with FNS experts. ResultsThe chatbot could accurately understand and respond to 60 different intents or keywords related to FNS. Themes such as training, guidance, and access emerged as crucial for guiding FNS initiatives and addressing implementation challenges, primarily related to human resources. The chatbot achieved a Global Content Validation Index of 0.88. ConclusionThe developed chatbot represents a significant advancement in supporting FNS practices within primary health care. Practice implicationBy providing an innovative, interactive, educational tool that is both accessible and reliable, this digital assistant has the potential to facilitate the operationalization of FNS practices, addressing the critical need for effective training and counseling in developing countries.

Artificial Intelligence in Medicine

Artificial intelligence in medicine refers to the use of machine learning models to help process medical data and provide medical professionals with important insights, improving health outcomes and patient experience. Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is rapidly becoming an integral part of modern healthcare. Therefore, artificial intelligence algorithms and other AI-powered applications are now used to support medical professionals in clinical settings and in ongoing research. There are several applications of Artificial intelligence in medicine, including applications to help detect and diagnose diseases; applications to treat diseases with the help of an AI-powered virtual assistant; AI applications in medical imaging; applications to increase the efficiency of clinical trials; and applications to accelerate drug development. The benefits of Artificial intelligence in medicine can be summarized in providing informed patient care, reducing errors, reducing care costs, and increasing doctor-patient engagement.

Caching Historical Embeddings in Conversational Search

Rapid response, namely, low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational settings. An observation with a potential to reduce latency asserts that conversational queries exhibit a temporal locality in the lists of documents retrieved. Motivated by this observation, we propose and evaluate a client-side document embedding cache, improving the responsiveness of conversational search systems. By leveraging state-of-the-art dense retrieval models to abstract document and query semantics, we cache the embeddings of documents retrieved for a topic introduced in the conversation, as they are likely relevant to successive queries. Our document embedding cache implements an efficient metric index, answering nearest-neighbor similarity queries by estimating the approximate result sets returned. We demonstrate the efficiency achieved using our cache via reproducible experiments based on Text Retrieval Conference Conversational Assistant Track datasets, achieving a hit rate of up to 75% without degrading answer quality. Our achieved high cache hit rates significantly improve the responsiveness of conversational systems while likewise reducing the number of queries managed on the search back-end.

Appropriateness and readability of Google Bard and ChatGPT-3.5 generated responses for surgical treatment of glaucoma.

To evaluate the appropriateness and readability of the medical knowledge provided by ChatGPT-3.5 and Google Bard, artificial-intelligence-powered conversational search engines, regarding surgical treatment for glaucoma. In this retrospective, cross-sectional study, 25 common questions related to the surgical management of glaucoma were asked on ChatGPT-3.5 and Google Bard. Glaucoma specialists graded the responses' appropriateness, and different scores assessed readability. Appropriate answers to the posed questions were obtained in 68% of the responses with Google Bard and 96% with ChatGPT-3.5. On average, the responses generated by Google Bard had a significantly lower proportion of sentences, having more than 30 and 20 syllables (23% and 52% respectively) compared to ChatGPT-3.5 (66% and 82% respectively), as noted by readability. Google Bard had significantly (p<0.0001) lower readability grade scores and significantly higher "Flesch Reading ease score", implying greater ease of readability amongst the answers generated by Google Bard. Many patients and their families turn to LLM chatbots for information, necessitating clear and accurate content. Assessments of online glaucoma information have shown variability in quality and readability, with institutional websites generally performing better than private ones. We found that ChatGPT-3.5, while precise, has lower readability than Google Bard, which is more accessible but less precise. For example, the Flesch Reading Ease Score was 57.6 for Google Bard and 22.6 for ChatGPT, indicating Google Bard's content is easier to read. Moreover, the Gunning Fog Index scores suggested that Google Bard's text is more suitable for a broader audience. ChatGPT's knowledge is limited to data up to 2021, whereas Google Bard, trained with real-time data, offers more current information. Further research is needed to evaluate these tools across various medical topics. The answers generated by ChatGPT-3.5™ AI are more accurate than the ones given by Google Bard. However, comprehension of ChatGPT-3.5™ answers may be difficult for the public with glaucoma. This study emphasized the importance of verifying the accuracy and clarity of online information that glaucoma patients rely on to make informed decisions about their ocular health. This is an exciting new area for patient education and health literacy.