An Overview of Chatbot Technology

Artificial intelligence in medical science: a review

Artificial intelligence (AI) is a technique to make intelligent machines, mainly by using smart computer programs. It is based on a statistical analysis of data or machine learning. Using machine learning, software algorithms are designed according to the desired application. These techniques are found to have the potential for advancement in the medical field by generating new and significant perceptions from the data generated using various types of healthcare tests. Artificial intelligence (AI) in medicine is of two types: virtual and physical. The virtual part decides the treatment using electronic health record systems using various sensors whereas the physical part assists robots to perform surgeries, implants, replacement of various organs, elderly care, etc. Using AI, a machine can examine various kinds of health care test reports in one go which could save the time, money, and increase the chances of the patient to be treated without any hassles. At present, artificial intelligence (AI) is used while deciding the treatment, and medications using various tools which could analyze X-rays, CT scans, MRIs, and any other data. During the COVID pandemic, there was a huge/massive demand for AI-supported technologies and many of those were created during that time. This study is focused on various applications of AI in healthcare.

Reliability and accuracy of artificial intelligence ChatGPT in providing information on ophthalmic diseases and management to patients

PURPOSE

To assess the accuracy of ophthalmic information provided by an artificial intelligence chatbot (ChatGPT).

METHODS

Five diseases from 8 subspecialties of Ophthalmology were assessed by ChatGPT version 3.5. Three questions were asked to ChatGPT for each disease: what is x?; how is x diagnosed?; how is x treated? (x = name of the disease). Responses were graded by comparing them to the American Academy of Ophthalmology (AAO) guidelines for patients, with scores ranging from -3 (unvalidated and potentially harmful to a patient's health or well-being if they pursue such a suggestion) to 2 (correct and complete).

MAIN OUTCOMES

Accuracy of responses from ChatGPT in response to prompts related to ophthalmic health information in the form of scores on a scale from -3 to 2.

RESULTS

Of the 120 questions, 93 (77.5%) scored ≥ 1. 27. (22.5%) scored ≤ -1; among these, 9 (7.5%) obtained a score of -3. The overall median score amongst all subspecialties was 2 for the question "What is x", 1.5 for "How is x diagnosed", and 1 for "How is x treated", though this did not achieve significance by Kruskal-Wallis testing.

CONCLUSIONS

Despite the positive scores, ChatGPT on its own still provides incomplete, incorrect, and potentially harmful information about common ophthalmic conditions, defined as the recommendation of invasive procedures or other interventions with potential for adverse sequelae which are not supported by the AAO for the disease in question. ChatGPT may be a valuable adjunct to patient education, but currently, it is not sufficient without concomitant human medical supervision.

Personalized Medical Terminology Learning Game: Guess the Term

Objective: Wordbot, a chatbot designed for gamified education, transforms the process of memorizing complex medical terminology into an engaging and enjoyable activity for medical students. Taking inspiration from the "guessing words" game, Wordbot aims to improve medical students' learning outcomes by making the vocabulary memorization process more memorable. Materials and Methods: Wordbot, which can be implemented on the LINE platform, was created for this research, specifically to improve medical terminology learning. Wordbot incorporated mobile devices and personal computer-compatible flashcard games with features such as user ranking and personalization to enhance motivation and optimize learning outcomes. In the experimental research setting, half of a total of 48 nursing students were randomly assigned to use Wordbot for 4 months, and the other half were assigned to a control group relying on self-study without the help of Wordbot. Both groups received pretest and post-test to assess their respective learning of medical terminology. In this study, a statistical t-test was used to analyze the results between the two groups. In addition, user usability testing was conducted to evaluate the usability of Wordbot and gather feedback on user experience. Results: The results of this study have demonstrated that Wordbot is effective in facilitating students learning of medical terminology. Students experienced a significant improvement in their knowledge of medical terminology. An average user usability test score of 83.25 indicated that users' satisfaction with Wordbot is high. Conclusion: Incorporating gamification and personalization elements in Wordbot can significantly improve the overall enjoyment of the learning process. By participating in diverse interactive activities, users can effectively enhance their proficiency in spelling, recognition, and speaking. Wordbot utilizes sophisticated algorithms to generate customized questions based on identified mistakes, which facilitate error identification and correction. The robust findings of this study overwhelmingly support Wordbot's role as a convenient and easily accessible tool for learning medical terminology. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Chang Gung Medical Foundation (Protocol code: 202201586B0, date of approval: 8 November 2022). We obtained informed consent from all of our study participants regarding their willingness to participate in this study.

The impact of chatbots based on large language models on second language vocabulary acquisition

In recent years, the integration of artificial intelligence (AI) and machine learning (ML) into education, particularly for Personalized Language Learning (PLL), has garnered significant attention. This approach tailors interventions to address the unique challenges faced by individual learners. Large Language Models (LLMs), including Chatbots, have demonstrated a substantial potential in automating and enhancing educational tasks, effectively capturing the complexity and diversity of human language. In this study, 52 foreign language students were randomly divided into two groups: one with the assistance of a Chatbot based on LLMs and one without. Both groups learned the same series of target words over eight weeks. Post-treatment assessments, including systematic observation and quantitative tests assessing both receptive and productive vocabulary knowledge, were conducted immediately after the study and again two weeks later. The findings demonstrate that employing an AI Chatbot based on LLMs significantly aids students in acquiring both receptive and productive vocabulary knowledge during their second language learning journey. Notably, Chatbots contribute to the long-term retention of productive vocabulary and facilitate incidental vocabulary learning. This study offers valuable insights into the practical benefits of LLM-based tools in language learning, with a specific emphasis on vocabulary development. Chatbots utilizing LLMs emerge as effective language learning aids. It emphasizes the importance of educators understanding the potential of these technologies in L2 vocabulary instruction and encourages the adoption of strategic teaching methods incorporating such tools.

General Characteristics and Design Taxonomy of Chatbots for COVID-19: Systematic Review

BACKGROUND

A conversational agent powered by artificial intelligence, commonly known as a chatbot, is one of the most recent innovations used to provide information and services during the COVID-19 pandemic. However, the multitude of conversational agents explicitly designed during the COVID-19 pandemic calls for characterization and analysis using rigorous technological frameworks and extensive systematic reviews.

OBJECTIVE

This study aims to describe the general characteristics of COVID-19 chatbots and examine their system designs using a modified adapted design taxonomy framework.

METHODS

We conducted a systematic review of the general characteristics and design taxonomy of COVID-19 chatbots, with 56 studies included in the final analysis. This review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select papers published between March 2020 and April 2022 from various databases and search engines.

RESULTS

Results showed that most studies on COVID-19 chatbot design and development worldwide are implemented in Asia and Europe. Most chatbots are also accessible on websites, internet messaging apps, and Android devices. The COVID-19 chatbots are further classified according to their temporal profiles, appearance, intelligence, interaction, and context for system design trends. From the temporal profile perspective, almost half of the COVID-19 chatbots interact with users for several weeks for >1 time and can remember information from previous user interactions. From the appearance perspective, most COVID-19 chatbots assume the expert role, are task oriented, and have no visual or avatar representation. From the intelligence perspective, almost half of the COVID-19 chatbots are artificially intelligent and can respond to textual inputs and a set of rules. In addition, more than half of these chatbots operate on a structured flow and do not portray any socioemotional behavior. Most chatbots can also process external data and broadcast resources. Regarding their interaction with users, most COVID-19 chatbots are adaptive, can communicate through text, can react to user input, are not gamified, and do not require additional human support. From the context perspective, all COVID-19 chatbots are goal oriented, although most fall under the health care application domain and are designed to provide information to the user.

CONCLUSIONS

The conceptualization, development, implementation, and use of COVID-19 chatbots emerged to mitigate the effects of a global pandemic in societies worldwide. This study summarized the current system design trends of COVID-19 chatbots based on 5 design perspectives, which may help developers conveniently choose a future-proof chatbot archetype that will meet the needs of the public in the face of growing demand for a better pandemic response.

Potential of AI-Driven Chatbots in Urology: Revolutionizing Patient Care Through Artificial Intelligence

PURPOSE OF REVIEW

Artificial intelligence (AI) chatbots have emerged as a potential tool to transform urology by improving patient care and physician efficiency. With an emphasis on their potential advantages and drawbacks, this literature review offers a thorough assessment of the state of AI-driven chatbots in urology today.

RECENT FINDINGS

The capacity of AI-driven chatbots in urology to give patients individualized and timely medical advice is one of its key advantages. Chatbots can help patients prioritize their symptoms and give advice on the best course of treatment. By automating administrative duties and offering clinical decision support, chatbots can also help healthcare providers. Before chatbots are widely used in urology, there are a few issues that need to be resolved. The precision of chatbot diagnoses and recommendations might be impacted by technical constraints like system errors and flaws. Additionally, issues regarding the security and privacy of patient data must be resolved, and chatbots must adhere to all applicable laws. Important issues that must be addressed include accuracy and dependability because any mistakes or inaccuracies could seriously harm patients. The final obstacle is resistance from patients and healthcare professionals who are hesitant to use new technology or who value in-person encounters. AI-driven chatbots have the potential to significantly improve urology care and efficiency. However, it is essential to thoroughly test and ensure the accuracy of chatbots, address privacy and security concerns, and design user-friendly chatbots that can integrate into existing workflows. By exploring various scenarios and examining the current literature, this review provides an analysis of the prospects and limitations of implementing chatbots in urology.

Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications

Although chatbots have existed for decades, the emergence of transformer-based large language models (LLMs) has captivated the world through the most recent wave of artificial intelligence chatbots, including ChatGPT. Transformers are a type of neural network architecture that enables better contextual understanding of language and efficient training on massive amounts of unlabeled data, such as unstructured text from the internet. As LLMs have increased in size, their improved performance and emergent abilities have revolutionized natural language processing. Since language is integral to human thought, applications based on LLMs have transformative potential in many industries. In fact, LLM-based chatbots have demonstrated human-level performance on many professional benchmarks, including in radiology. LLMs offer numerous clinical and research applications in radiology, several of which have been explored in the literature with encouraging results. Multimodal LLMs can simultaneously interpret text and images to generate reports, closely mimicking current diagnostic pathways in radiology. Thus, from requisition to report, LLMs have the opportunity to positively impact nearly every step of the radiology journey. Yet, these impressive models are not without limitations. This article reviews the limitations of LLMs and mitigation strategies, as well as potential uses of LLMs, including multimodal models. Also reviewed are existing LLM-based applications that can enhance efficiency in supervised settings.

Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation

BACKGROUND

Chatbots enable users to have humanlike conversations on various topics and can vary widely in complexity and functionality. An area of research priority in chatbots is democratizing chatbots to all, removing barriers to entry, such as financial ones, to help make chatbots a possibility for the wider global population to improve access to information, help reduce the digital divide between nations, and improve areas of public good (eg, health communication). Chatbots in this space may help create the potential for improved health outcomes, potentially alleviating some of the burdens on health care providers and systems to be the sole voices of outreach to public health.

OBJECTIVE

This study explored the feasibility of developing a chatbot using approaches that are accessible in low- and middle-resource settings, such as using technology that is low cost, can be developed by nonprogrammers, and can be deployed over social media platforms to reach the broadest-possible audience without the need for a specialized technical team.

METHODS

This study is presented in 2 parts. First, we detailed the design and development of a chatbot, VWise, including the resources used and development considerations for the conversational model. Next, we conducted a case study of 33 participants who engaged in a pilot with our chatbot. We explored the following 3 research questions: (1) Is it feasible to develop and implement a chatbot addressing a public health issue with only minimal resources? (2) What is the participants' experience with using the chatbot? (3) What kinds of measures of engagement are observed from using the chatbot?

RESULTS

A high level of engagement with the chatbot was demonstrated by the large number of participants who stayed with the conversation to its natural end (n=17, 52%), requested to see the free online resource, selected to view all information about a given concern, and returned to have a dialogue about a second concern (n=12, 36%).

CONCLUSIONS

This study explored the feasibility of and the design and development considerations for a chatbot, VWise. Our early findings from this initial pilot suggest that developing a functioning and low-cost chatbot is feasible, even in low-resource environments. Our results show that low-resource environments can enter the health communication chatbot space using readily available human and technical resources. However, despite these early indicators, many limitations exist in this study and further work with a larger sample size and greater diversity of participants is needed. This study represents early work on a chatbot in its virtual infancy. We hope this study will help provide those who feel chatbot access may be out of reach with a useful guide to enter this space, enabling more democratized access to chatbots for all.

Systematic review and meta-analysis of AI-based conversational agents for promoting mental health and well-being

Conversational artificial intelligence (AI), particularly AI-based conversational agents (CAs), is gaining traction in mental health care. Despite their growing usage, there is a scarcity of comprehensive evaluations of their impact on mental health and well-being. This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health and factors influencing their effectiveness and user experience. Twelve databases were searched for experimental studies of AI-based CAs' effects on mental illnesses and psychological well-being published before May 26, 2023. Out of 7834 records, 35 eligible studies were identified for systematic review, out of which 15 randomized controlled trials were included for meta-analysis. The meta-analysis revealed that AI-based CAs significantly reduce symptoms of depression (Hedge's g 0.64 [95% CI 0.17-1.12]) and distress (Hedge's g 0.7 [95% CI 0.18-1.22]). These effects were more pronounced in CAs that are multimodal, generative AI-based, integrated with mobile/instant messaging apps, and targeting clinical/subclinical and elderly populations. However, CA-based interventions showed no significant improvement in overall psychological well-being (Hedge's g 0.32 [95% CI -0.13 to 0.78]). User experience with AI-based CAs was largely shaped by the quality of human-AI therapeutic relationships, content engagement, and effective communication. These findings underscore the potential of AI-based CAs in addressing mental health issues. Future research should investigate the underlying mechanisms of their effectiveness, assess long-term effects across various mental health outcomes, and evaluate the safe integration of large language models (LLMs) in mental health care.

Review: The prevailing mathematical modeling classifications and paradigms to support the advancement of sustainable animal production

Mathematical modeling is typically framed as the art of reductionism of scientific knowledge into an arithmetical layout. However, most untrained people get the art of modeling wrong and end up neglecting it because modeling is not simply about writing equations and generating numbers through simulations. Models tell not only about a story; they are spoken to by the circumstances under which they are envisioned. They guide apprentice and experienced modelers to build better models by preventing known pitfalls and invalid assumptions in the virtual world and, most importantly, learn from them through simulation and identify gaps in pushing scientific knowledge further. The power of the human mind is well-documented for idealizing concepts and creating virtual reality models, and as our hypotheses grow more complicated and more complex data become available, modeling earns more noticeable footing in biological sciences. The fundamental modeling paradigms include discrete-events, dynamic systems, agent-based (AB), and system dynamics (SD). The source of knowledge is the most critical step in the model-building process regardless of the paradigm, and the necessary expertise includes (a) clear and concise mental concepts acquired through different ways that provide the fundamental structure and expected behaviors of the model and (b) numerical data necessary for statistical analysis, not for building the model. The unreasonable effectiveness of models to grow scientific learning and knowledge in sciences arise because different researchers would model the same problem differently, given their knowledge and experiential background, leading to choosing different variables and model structures. Secondly, different researchers might use different paradigms and even unalike mathematics to resolve the same problem; thus, model needs are intrinsic to their perceived assumptions and structures. Thirdly, models evolve as the scientific community knowledge accumulates and matures over time, hopefully resulting in improved modeling efforts; thus, the perfect model is fictional. Some paradigms are most appropriate for macro, high abstraction with less detailed-oriented scenarios, while others are most suitable for micro, low abstraction with higher detailed-oriented strategies. Modern hybridization aggregating artificial intelligence (AI) to mathematical models can become the next technological wave in modeling. AI can be an integral part of the SD/AB models and, before long, write the model code by itself. Success and failures in model building are more related to the ability of the researcher to interpret the data and understand the underlying principles and mechanisms to formulate the correct relationship among variables rather than profound mathematical knowledge.

ChatGPT and large language models in orthopedics: from education and surgery to research

ChatGPT has quickly popularized since its release in November 2022. Currently, large language models (LLMs) and ChatGPT have been applied in various domains of medical science, including in cardiology, nephrology, orthopedics, ophthalmology, gastroenterology, and radiology. Researchers are exploring the potential of LLMs and ChatGPT for clinicians and surgeons in every domain. This study discusses how ChatGPT can help orthopedic clinicians and surgeons perform various medical tasks. LLMs and ChatGPT can help the patient community by providing suggestions and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance and expand the field of orthopedics, including orthopedic education, surgery, and research, is explored. Present LLMs have several shortcomings, which are discussed herein. However, next-generation and future domain-specific LLMs are expected to be more potent and transform patients' quality of life.

Review: When worlds collide - poultry modeling in the 'Big Data' era

Within poultry production systems, models have provided vital decision support, opportunity analysis, and performance optimization capabilities to nutritionists and producers for decades. In recent years, due to the advancement of digital and sensor technologies, 'Big Data' streams have emerged, optimally positioned to be analyzed by machine-learning (ML) modeling approaches, with strengths in forecasting and prediction. This review explores the evolution of empirical and mechanistic models in poultry production systems, and how these models may interact with new digital tools and technologies. This review will also examine the emergence of ML and Big Data in the poultry production sector, and the emergence of precision feeding and automation of poultry production systems. There are several promising directions for the field, including: (1) application of Big Data analytics (e.g., sensor-based technologies, precision feeding systems) and ML methodologies (e.g., unsupervised and supervised learning algorithms) to feed more precisely to production targets given a 'known' individual animal, and (2) combination and hybridization of data-driven and mechanistic modeling approaches to bridge decision support with improved forecasting capabilities.

ChatGPT in Drug Discovery: A Case Study on Anticocaine Addiction Drug Development with Chatbots

The birth of ChatGPT, a cutting-edge language model-based chatbot developed by OpenAI, ushered in a new era in AI. However, due to potential pitfalls, its role in rigorous scientific research is not clear yet. This paper vividly showcases its innovative application within the field of drug discovery. Focused specifically on developing anticocaine addiction drugs, the study employs GPT-4 as a virtual guide, offering strategic and methodological insights to researchers working on generative models for drug candidates. The primary objective is to generate optimal drug-like molecules with desired properties. By leveraging the capabilities of ChatGPT, the study introduces a novel approach to the drug discovery process. This symbiotic partnership between AI and researchers transforms how drug development is approached. Chatbots become facilitators, steering researchers toward innovative methodologies and productive paths for creating effective drug candidates. This research sheds light on the collaborative synergy between human expertise and AI assistance, wherein ChatGPT's cognitive abilities enhance the design and development of pharmaceutical solutions. This paper not only explores the integration of advanced AI in drug discovery but also reimagines the landscape by advocating for AI-powered chatbots as trailblazers in revolutionizing therapeutic innovation.

"Chatting with ChatGPT": Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model

Open AI's ChatGPT has emerged as a popular AI language model that can engage in natural language conversations with users. Based on a qualitative research approach using semistructured interviews with 32 ChatGPT users from India, this study examined the factors influencing users' acceptance and use of ChatGPT using the unified theory of acceptance and usage of technology (UTAUT) model. The study results demonstrated that the four factors of UTAUT, along with two extended constructs, i.e. perceived interactivity and privacy concerns, can explain users' interaction and engagement with ChatGPT. The study also found that age and experience can moderate the impact of various factors on the use of ChatGPT. The theoretical and practical implications of the study were also discussed.

Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science

The release of ChatGPT has initiated new thinking about AI-based Chatbot and its application and has drawn huge public attention worldwide. Researchers and doctors have started thinking about the promise and application of AI-related large language models in medicine during the past few months. Here, the comprehensive review highlighted the overview of Chatbot and ChatGPT and their current role in medicine. Firstly, the general idea of Chatbots, their evolution, architecture, and medical use are discussed. Secondly, ChatGPT is discussed with special emphasis of its application in medicine, architecture and training methods, medical diagnosis and treatment, research ethical issues, and a comparison of ChatGPT with other NLP models are illustrated. The article also discussed the limitations and prospects of ChatGPT. In the future, these large language models and ChatGPT will have immense promise in healthcare. However, more research is needed in this direction.

Exploring the Potential of Chatbots in Critical Care Nephrology

The exponential growth of artificial intelligence (AI) has allowed for its integration into multiple sectors, including, notably, healthcare. Chatbots have emerged as a pivotal resource for improving patient outcomes and assisting healthcare practitioners through various AI-based technologies. In critical care, kidney-related conditions play a significant role in determining patient outcomes. This article examines the potential for integrating chatbots into the workflows of critical care nephrology to optimize patient care. We detail their specific applications in critical care nephrology, such as managing acute kidney injury, alert systems, and continuous renal replacement therapy (CRRT); facilitating discussions around palliative care; and bolstering collaboration within a multidisciplinary team. Chatbots have the potential to augment real-time data availability, evaluate renal health, identify potential risk factors, build predictive models, and monitor patient progress. Moreover, they provide a platform for enhancing communication and education for both patients and healthcare providers, paving the way for enriched knowledge and honed professional skills. However, it is vital to recognize the inherent challenges and limitations when using chatbots in this domain. Here, we provide an in-depth exploration of the concerns tied to chatbots' accuracy, dependability, data protection and security, transparency, potential algorithmic biases, and ethical implications in critical care nephrology. While human discernment and intervention are indispensable, especially in complex medical scenarios or intricate situations, the sustained advancements in AI signal that the integration of precision-engineered chatbot algorithms within critical care nephrology has considerable potential to elevate patient care and pivotal outcome metrics in the future.

Development of immersive learning framework (ILF) in achieving the goals of higher education: measuring the impact using a pre-post design

Emerging technological tools like Artificial Intelligence-based Chatbots, digital educational alternatives and market-driven educational systems pose a challenge to the fundamental aim of the higher education system; comprehensive education for well-being. Therefore, this research aims to devise and evaluate strategies to impart new-age competencies to innovate socially and morally appropriate solutions in a modern competitive innovative society. The 8-month-long immersive learning framework (ILF), was designed based on the volatility, uncertainty, complexity, and ambiguity (VUCA) paradigm. The framework was evaluated with 133 newly joined postgraduate students doing their science or arts programmes from a higher education institution in Kerala, India. The outcome variables included well-being, depressive symptoms, personality patterns, and sub-domains of philosophy of human nature. The follow-up scores showed a significant improvement in well-being (Mean difference: 1.15, p = 0.005), trustworthiness (Mean difference: 14.74, p = 0.000), strength of will (Mean difference: 10.11, p = 0.000), altruism (Mean difference: 12.85, p = 0.000), and independence (Mean difference: 11.93, p = 0.000). Depression scores did not improve significantly. However, the intervention shielded them from the adjustment issues that often accompany any transition. The ILF framework can help students develop their personal and professional selves if it is implemented collaboratively in a reflective setting. It can also instil moral rectitude and a prosocial mindset.

Large language models: Are artificial intelligence-based chatbots a reliable source of patient information for spinal surgery?

PURPOSE

Large language models (LLM) have recently attracted attention because of their enormous performance. Based on artificial intelligence, LLM enable dialogic communication using quasi-natural language that approximates the quality of human communication. Thus, LLM could play an important role for patients to become informed. To evaluate the validity of an LLM in providing medical information, we used one of the first high-performance LLM (ChatGPT) on the clinical example of acute lumbar disc herniation (LDH).

METHODS

Twenty-four spinal surgeons experienced in LDH surgery directed questions to ChatGPT about the clinical picture of LDH from a patient's perspective. They evaluated the quality of ChatGPT responses and its potential use in medical communication. The responses were compared with the information content of a standard informed consent form.

RESULTS

ChatGPT provided good results in terms of comprehensibility, specificity, and satisfaction of responses and in terms of medical accuracy and completeness. ChatGPT was not able to provide all the information that was provided in the informed consent form, but did communicate information that was not listed there. In some cases, albeit minor, ChatGPT made medically inaccurate claims, such as listing kyphoplasty and vertebroplasty as surgical options for LDH.

CONCLUSION

With the incipient use of artificial intelligence in communication, LLM will certainly become increasingly important to patients. Even if LLM are unlikely to play a role in clinical communication between physicians and patients at the moment, the opportunities-but also the risks-of this novel technology should be alertly monitored.

The reputational and ethical consequences of deceptive chatbot use

The use of chatbots is becoming widespread as they offer significant economic opportunities. At the same time, however, customers seem to prefer interacting with human operators when making inquiries and as a result are not as cooperative with chatbots when their use is known. This specific situation creates an incentive for organizations to use chatbots without disclosing this to customers. Will this deceptive practice harm the reputation of the organization, and the employees who work for them? Across four experimental studies, we demonstrate that prospective customers, who interact with an organization using chatbots, perceive the organization to be less ethical if the organization does not disclose the information about the chatbot to their customers (Study 1). Moreover, employees that work for an organization which requires them to facilitate the deceptive use of a chatbot exhibit greater turnover intentions (Study 2) and receive worse job opportunities from recruiters in both a hypothetical experimental setting (Study 3) and from professional job recruiters in the field (Study 4). These results highlight that using chatbots deceptively has far reaching negative effects, which begin with the organization and ultimately impact their customers and the employees that work for them.

Empowering Preventive Care with GECA Chatbot

Chatbots have become increasingly popular in the healthcare industry. In the area of preventive care, chatbots can provide personalized and timely solutions that aid individuals in maintaining their well-being and forestalling the development of chronic conditions. This paper presents GECA, a chatbot designed specifically for preventive care, that offers information, advice, and monitoring to patients who are undergoing home treatment, providing a cost-effective, personalized, and engaging solution. Moreover, its adaptable architecture enables extension to other diseases and conditions seamlessly. The chatbot's bilingual capabilities enhance accessibility for a wider range of users, including those with reading or writing difficulties, thereby improving the overall user experience. GECA's ability to connect with external resources offers a higher degree of personalization, which is a crucial aspect in engaging users effectively. The integration of standards and security protocols in these connections allows patient privacy, security and smooth adaptation to emerging healthcare information sources. GECA has demonstrated a remarkable level of accuracy and precision in its interactions with the diverse features, boasting an impressive 97% success rate in delivering accurate responses. Presently, preparations are underway for a pilot project at a Portuguese hospital that will conduct exhaustive testing and evaluate GECA, encompassing aspects such as its effectiveness, efficiency, quality, goal achievability, and user satisfaction.

Chatbots to Improve Sexual and Reproductive Health: Realist Synthesis

BACKGROUND

Digital technologies may improve sexual and reproductive health (SRH) across diverse settings. Chatbots are computer programs designed to simulate human conversation, and there is a growing interest in the potential for chatbots to provide responsive and accurate information, counseling, linkages to products and services, or a companion on an SRH journey.

OBJECTIVE

This review aimed to identify assumptions about the value of chatbots for SRH and collate the evidence to support them.

METHODS

We used a realist approach that starts with an initial program theory and generates causal explanations in the form of context, mechanism, and outcome configurations to test and develop that theory. We generated our program theory, drawing on the expertise of the research team, and then searched the literature to add depth and develop this theory with evidence.

RESULTS

The evidence supports our program theory, which suggests that chatbots are a promising intervention for SRH information and service delivery. This is because chatbots offer anonymous and nonjudgmental interactions that encourage disclosure of personal information, provide complex information in a responsive and conversational tone that increases understanding, link to SRH conversations within web-based and offline social networks, provide immediate support or service provision 24/7 by automating some tasks, and provide the potential to develop long-term relationships with users who return over time. However, chatbots may be less valuable where people find any conversation about SRH (even with a chatbot) stigmatizing, for those who lack confidential access to digital devices, where conversations do not feel natural, and where chatbots are developed as stand-alone interventions without reference to service contexts.

CONCLUSIONS

Chatbots in SRH could be developed further to automate simple tasks and support service delivery. They should prioritize achieving an authentic conversational tone, which could be developed to facilitate content sharing in social networks, should support long-term relationship building with their users, and should be integrated into wider service networks.

Modeling the Conversation with Digital Health Assistants in Adherence Apps: Some Considerations on the Similarities and Differences with Familiar Medical Encounters

Digital health assistants (DHAs) are conversational agents incorporated into health systems' interfaces, exploiting an intuitive interaction format appreciated by the users. At the same time, however, their conversational format can evoke interactional practices typical of health encounters with human doctors that might misguide the users. Awareness of the similarities and differences between novel mediated encounters and more familiar ones helps designers avoid unintended expectations and leverage suitable ones. Focusing on adherence apps, we analytically discuss the structure of DHA-patient encounters against the literature on physician-patient encounters and the specific affordances of DHAs. We synthesize our discussion into a design checklist and add some considerations about DHA with unconstrained natural language interfaces.

Meeting the Behavioral Health Needs of Health Care Workers During COVID-19 by Leveraging Chatbot Technology: Development and Usability Study

BACKGROUND

During the COVID-19 pandemic, health care systems were faced with the urgent need to implement strategies to address the behavioral health needs of health care workers. A primary concern of any large health care system is developing an easy-to-access, streamlined system of triage and support despite limited behavioral health resources.

OBJECTIVE

This study provides a detailed description of the design and implementation of a chatbot program designed to triage and facilitate access to behavioral health assessment and treatment for the workforce of a large academic medical center. The University of California, San Francisco (UCSF) Faculty, Staff, and Trainee Coping and Resiliency Program (UCSF Cope) aimed to provide timely access to a live telehealth navigator for triage and live telehealth assessment and treatment, curated web-based self-management tools, and nontreatment support groups for those experiencing stress related to their unique roles.

METHODS

In a public-private partnership, the UCSF Cope team built a chatbot to triage employees based on behavioral health needs. The chatbot is an algorithm-based, automated, and interactive artificial intelligence conversational tool that uses natural language understanding to engage users by presenting a series of questions with simple multiple-choice answers. The goal of each chatbot session was to guide users to services that were appropriate for their needs. Designers developed a chatbot data dashboard to identify and follow trends directly through the chatbot. Regarding other program elements, website user data were collected monthly and participant satisfaction was gathered for each nontreatment support group.

RESULTS

The UCSF Cope chatbot was rapidly developed and launched on April 20, 2020. As of May 31, 2022, a total of 10.88% (3785/34,790) of employees accessed the technology. Among those reporting any form of psychological distress, 39.7% (708/1783) of employees requested in-person services, including those who had an existing provider. UCSF employees responded positively to all program elements. As of May 31, 2022, the UCSF Cope website had 615,334 unique users, with 66,585 unique views of webinars and 601,471 unique views of video shorts. All units across UCSF were reached by UCSF Cope staff for special interventions, with >40 units requesting these services. Town halls were particularly well received, with >80% of attendees reporting the experience as helpful.

CONCLUSIONS

UCSF Cope used chatbot technology to incorporate individualized behavioral health triage, assessment, treatment, and general emotional support for an entire employee base (N=34,790). This level of triage for a population of this size would not have been possible without the use of chatbot technology. The UCSF Cope model has the potential to be scaled, adapted, and implemented across both academically and nonacademically affiliated medical settings.

Chatbot Language - crowdsource perceptions and reactions to dialogue systems to inform dialogue design decisions

Conversational User Interfaces (CUI) are widely used, with about 1.8 billion users worldwide in 2020. For designing and building CUI, dialogue designers have to decide on how the CUI communicates with users and what dialogue strategies to pursue (e.g. reactive vs. proactive). Dialogue strategies can be evaluated in user tests by comparing user perceptions and reactions to different dialogue strategies. Simulating CUI and running them online, for example on crowdsourcing websites, is an attractive avenue to collecting user perceptions and reactions, as they can be gathered time- and cost-effectively. However, developing and deploying a CUI on a crowd sourcing platform can be laborious and requires technical proficiency from researchers. We present Chatbot Language (CBL) as a framework to quickly develop and deploy CUI on crowd sourcing platforms, without requiring a technical background. CBL is a library with specialized CUI functionality, which is based on the high-level language JavaScript. In addition, CBL provides scripts that use the API of the crowd sourcing platform Mechanical Turk (MT) in order to (a) create MT Human Intelligence Tasks (HITs) and (b) retrieve the results of those HITs. We used CBL to run experiments on MT and present a sample workflow as well as an example experiment. CBL is freely available and we discuss how CBL can be used now and may be further developed in the future.

An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews

BACKGROUND

Chatbots are an emerging technology that show potential for mental health care apps to enable effective and practical evidence-based therapies. As this technology is still relatively new, little is known about recently developed apps and their characteristics and effectiveness.

OBJECTIVE

In this study, we aimed to provide an overview of the commercially available popular mental health chatbots and how they are perceived by users.

METHODS

We conducted an exploratory observation of 10 apps that offer support and treatment for a variety of mental health concerns with a built-in chatbot feature and qualitatively analyzed 3621 consumer reviews from the Google Play Store and 2624 consumer reviews from the Apple App Store.

RESULTS

We found that although chatbots' personalized, humanlike interactions were positively received by users, improper responses and assumptions about the personalities of users led to a loss of interest. As chatbots are always accessible and convenient, users can become overly attached to them and prefer them over interacting with friends and family. Furthermore, a chatbot may offer crisis care whenever the user needs it because of its 24/7 availability, but even recently developed chatbots lack the understanding of properly identifying a crisis. Chatbots considered in this study fostered a judgment-free environment and helped users feel more comfortable sharing sensitive information.

CONCLUSIONS

Our findings suggest that chatbots have great potential to offer social and psychological support in situations where real-world human interaction, such as connecting to friends or family members or seeking professional support, is not preferred or possible to achieve. However, there are several restrictions and limitations that these chatbots must establish according to the level of service they offer. Too much reliance on technology can pose risks, such as isolation and insufficient assistance during times of crisis. Recommendations for customization and balanced persuasion to inform the design of effective chatbots for mental health support have been outlined based on the insights of our findings.

Conversational Artificial Intelligence in Psychotherapy: A New Therapeutic Tool or Agent?

Conversational artificial intelligence (CAI) presents many opportunities in the psychotherapeutic landscape-such as therapeutic support for people with mental health problems and without access to care. The adoption of CAI poses many risks that need in-depth ethical scrutiny. The objective of this paper is to complement current research on the ethics of AI for mental health by proposing a holistic, ethical, and epistemic analysis of CAI adoption. First, we focus on the question of whether CAI is rather a tool or an agent. This question serves as a framework for the subsequent ethical analysis of CAI focusing on topics of (self-) knowledge, (self-)understanding, and relationships. Second, we propose further conceptual and ethical analysis regarding human-AI interaction and argue that CAI cannot be considered as an equal partner in a conversation as is the case with a human therapist. Instead, CAI's role in a conversation should be restricted to specific functions.

Exploring the potential of mobile health interventions to address behavioural risk factors for the prevention of non-communicable diseases in Asian populations: a qualitative study

BACKGROUND

Changing lifestyle patterns over the last decades have seen growing numbers of people in Asia affected by non-communicable diseases and common mental health disorders, including diabetes, cancer, and/or depression. Interventions targeting healthy lifestyle behaviours through mobile technologies, including new approaches such as chatbots, may be an effective, low-cost approach to prevent these conditions. To ensure uptake and engagement with mobile health interventions, however, it is essential to understand the end-users' perspectives on using such interventions. The aim of this study was to explore perceptions, barriers, and facilitators to the use of mobile health interventions for lifestyle behaviour change in Singapore.

METHODS

Six virtual focus group discussions were conducted with a total of 34 participants (mean ± SD; aged 45 ± 3.6 years; 64.7% females). Focus group recordings were transcribed verbatim and analysed using an inductive thematic analysis approach, followed by deductive mapping according to perceptions, barriers, facilitators, mixed factors, or strategies.

RESULTS

Five themes were identified: (i) holistic wellbeing is central to healthy living (i.e., the importance of both physical and mental health); (ii) encouraging uptake of a mobile health intervention is influenced by factors such as incentives and government backing; (iii) trying out a mobile health intervention is one thing, sticking to it long term is another and there are key factors, such as personalisation and ease of use that influence sustained engagement with mobile health interventions; (iv) perceptions of chatbots as a tool to support healthy lifestyle behaviour are influenced by previous negative experiences with chatbots, which might hamper uptake; and (v) sharing health-related data is OK, but with conditions such as clarity on who will have access to the data, how it will be stored, and for what purpose it will be used.

CONCLUSIONS

Findings highlight several factors that are relevant for the development and implementation of mobile health interventions in Singapore and other Asian countries. Recommendations include: (i) targeting holistic wellbeing, (ii) tailoring content to address environment-specific barriers, (iii) partnering with government and/or local (non-profit) institutions in the development and/or promotion of mobile health interventions, (iv) managing expectations regarding the use of incentives, and (iv) identifying potential alternatives or complementary approaches to the use of chatbots, particularly for mental health.

Overview of Early ChatGPT's Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts

ChatGPT, an artificial intelligence chatbot, has rapidly gained prominence in various domains, including medical education and healthcare literature. This hybrid narrative review, conducted collaboratively by human authors and ChatGPT, aims to summarize and synthesize the current knowledge of ChatGPT in the indexed medical literature during its initial four months. A search strategy was employed in PubMed and EuropePMC databases, yielding 65 and 110 papers, respectively. These papers focused on ChatGPT's impact on medical education, scientific research, medical writing, ethical considerations, diagnostic decision-making, automation potential, and criticisms. The findings indicate a growing body of literature on ChatGPT's applications and implications in healthcare, highlighting the need for further research to assess its effectiveness and ethical concerns.

A novel use of an artificially intelligent Chatbot and a live, synchronous virtual question-and answer session for fellowship recruitment

INTRODUCTION

Academic departments universally communicate information about their programs using static websites. In addition to websites, some programs have even ventured out into social media (SM). These bidirectional forms of SM interaction show great promise; even hosting a live Question and Answer (Q&A) session has the potential for program branding. Artificial Intelligence (AI) usage in the form of a chatbot has expanded on websites and in SM. The potential use of chatbots, for the purposes of trainee recruitment, is novel and underutilized. With this pilot study, we aimed to answer the question; can the use of an Artificially Intelligent Chatbot and a Virtual Question-and-Answer Session aid in recruitment in a Post-COVID-19 era?

METHODS

We held three structured Question-and-Answer Sessions over a period of 2 weeks. This preliminary study was performed after completion of the three Q&A sessions, in March-May, 2021. All 258 applicants to the pain fellowship program were invited via email to participate in the survey after attending one of the Q&A sessions. A 16-item survey assessing participants' perception of the chatbot was administered.

RESULTS

Forty-eight pain fellowship applicants completed the survey, for an average response rate of 18.6%. In all, 35 (73%) of survey respondents had used the website chatbot, and 84% indicated that it had found them the information they were seeking.

CONCLUSION

We employed an artificially intelligent chatbot on the department website to engage in a bidirectional exchange with users to adapt to changes brought on by the pandemic. SM engagement via chatbot and Q&A sessions can leave a favorable impression and improve the perception of a program.

Prototyping a Chatbot for Site Managers Using Building Information Modeling (BIM) and Natural Language Understanding (NLU) Techniques

Amidst the domestic labor shortage and worldwide pandemic in recent years, there has been an urgent need for a digital means that allows construction site workers, particularly site managers, to obtain information more efficiently in support of their daily managerial tasks. For workers who move around the site, traditional software applications that rely on a form-based interface and require multiple finger movements such as key hits and clicks can be inconvenient and reduce their willingness to use such applications. Conversational AI, also known as a chatbot, can improve the ease of use and usability of a system by providing an intuitive interface for user input. This study presents a demonstrative Natural Language Understanding (NLU) model and prototypes an AI-based chatbot for site managers to inquire about building component dimensions during their daily routines. Building Information Modeling (BIM) techniques are also applied to implement the answering module of the chatbot. The preliminary testing results show that the chatbot can successfully predict the intents and entities behind the inquiries raised by site managers with satisfactory accuracy for both intent prediction and the answer. These results provide site managers with alternative means to retrieve the information they need.

Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study

Purpose

The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery.

Methods

First, we developed DR-COVID with an ensemble NLP model on the Telegram platform (https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies; (B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy; (B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data.

Results

Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826-0.851] and 0.922 [95% CI: 0.913-0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911-0.925] and 0.960 [95% CI: 0.955-0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12-2.15 s across three devices tested.

Conclusion

DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era.

Interacting with chatbots later in life: A technology acceptance perspective in COVID-19 pandemic situation

Introduction

Within the technological development path, chatbots are considered an important tool for economic and social entities to become more efficient and to develop customer-centric experiences that mimic human behavior. Although artificial intelligence is increasingly used, there is a lack of empirical studies that aim to understand consumers' experience with chatbots. Moreover, in a context characterized by constant population aging and an increased life-expectancy, the way aging adults perceive technology becomes of great interest. However, based on the digital divide (unequal access to technology, knowledge, and resources), and since young adults (aged between 18 and 34 years old) are considered to have greater affinity for technology, most of the research is dedicated to their perception. The present paper investigates the way chatbots are perceived by middle-aged and aging adults in Romania.

Methods

An online opinion survey has been conducted. The age-range of the subjects is 40-78 years old, a convenience sampling technique being used (N = 235). The timeframe of the study is May-June 2021. Thus, the COVID-19 pandemic is the core context of the research. A covariance-based structural equation modelling (CB-SEM) has been used to test the theoretical assumptions as it is a procedure used for complex conceptual models and theory testing.

Results

The results show that while perceived ease of use is explained by the effort, the competence, and the perceive external control in interacting with chatbots, perceived usefulness is supported by the perceived ease of use and subjective norms. Furthermore, individuals are likely to further use chatbots (behavioral intention) if they consider this interaction useful and if the others' opinion is in favor of using it. Gender and age seem to have no effect on behavioral intention. As studies on chatbots and aging adults are few and are mainly investigating reactions in the healthcare domain, this research is one of the first attempts to better understand the way chatbots in a not domain-specific context are perceived later in life. Likewise, judging from a business perspective, the results can help economic and social organizations to improve and adapt AI-based interaction for the aging customers.

User experience with a parenting chatbot micro intervention

Background

The use of chatbots to address mental health conditions have become increasingly popular in recent years. However, few studies aimed to teach parenting skills through chatbots, and there are no reports on parental user experience. Aim: This study aimed to assess the user experience of a parenting chatbot micro intervention to teach how to praise children in a Spanish-speaking country.

Methods

A sample of 89 parents were assigned to the chatbot micro intervention as part of a randomized controlled trial study. Completion rates, engagement, satisfaction, net promoter score, and acceptability were analyzed.

Results

66.3% of the participants completed the intervention. Participants exchanged an average of 49.8 messages (SD = 1.53), provided an average satisfaction score of 4.19 (SD = .79), and reported that they would recommend the chatbot to other parents (net promoter score = 4.63/5; SD = .66). Acceptability level was high (ease of use = 4.66 [SD = .73]; comfortability = 4.76 [SD = .46]; lack of technical problems = 4.69 [SD = .59]; interactivity = 4.51 [SD = .77]; usefulness for everyday life = 4.75 [SD = .54]).

Conclusions

Overall, users completed the intervention at a high rate, engaged with the chatbot, were satisfied, would recommend it to others, and reported a high level of acceptability. Chatbots have the potential to teach parenting skills however research on the efficacy of parenting chatbot interventions is needed.

An artificially intelligent, natural language processing chatbot designed to promote COVID-19 vaccination: A proof-of-concept pilot study

Objective

Our goal is to establish the feasibility of using an artificially intelligent chatbot in diverse healthcare settings to promote COVID-19 vaccination.

Methods

We designed an artificially intelligent chatbot deployed via short message services and web-based platforms. Guided by communication theories, we developed persuasive messages to respond to users' COVID-19-related questions and encourage vaccination. We implemented the system in healthcare settings in the U.S. between April 2021 and March 2022 and logged the number of users, topics discussed, and information on system accuracy in matching responses to user intents. We regularly reviewed queries and reclassified responses to better match responses to query intents as COVID-19 events evolved.

Results

A total of 2479 users engaged with the system, exchanging 3994 COVID-19 relevant messages. The most popular queries to the system were about boosters and where to get a vaccine. The system's accuracy rate in matching responses to user queries ranged from 54% to 91.1%. Accuracy lagged when new information related to COVID emerged, such as that related to the Delta variant. Accuracy increased when we added new content to the system.

Conclusions

It is feasible and potentially valuable to create chatbot systems using AI to facilitate access to current, accurate, complete, and persuasive information on infectious diseases. Such a system can be adapted to use with patients and populations needing detailed information and motivation to act in support of their health.

The Development and Use of Chatbots in Public Health: Scoping Review

Background

Chatbots are computer programs that present a conversation-like interface through which people can access information and services. The COVID-19 pandemic has driven a substantial increase in the use of chatbots to support and complement traditional health care systems. However, despite the uptake in their use, evidence to support the development and deployment of chatbots in public health remains limited. Recent reviews have focused on the use of chatbots during the COVID-19 pandemic and the use of conversational agents in health care more generally. This paper complements this research and addresses a gap in the literature by assessing the breadth and scope of research evidence for the use of chatbots across the domain of public health.

Objective

This scoping review had 3 main objectives: (1) to identify the application domains in public health in which there is the most evidence for the development and use of chatbots; (2) to identify the types of chatbots that are being deployed in these domains; and (3) to ascertain the methods and methodologies by which chatbots are being evaluated in public health applications. This paper explored the implications for future research on the development and deployment of chatbots in public health in light of the analysis of the evidence for their use.

Methods

Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines for scoping reviews, relevant studies were identified through searches conducted in the MEDLINE, PubMed, Scopus, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Open Grey databases from mid-June to August 2021. Studies were included if they used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact.

Results

Of the 1506 studies identified, 32 were included in the review. The results show a substantial increase in the interest of chatbots in the past few years, shortly before the pandemic. Half (16/32, 50%) of the research evaluated chatbots applied to mental health or COVID-19. The studies suggest promise in the application of chatbots, especially to easily automated and repetitive tasks, but overall, the evidence for the efficacy of chatbots for prevention and intervention across all domains is limited at present.

Conclusions

More research is needed to fully understand the effectiveness of using chatbots in public health. Concerns with the clinical, legal, and ethical aspects of the use of chatbots for health care are well founded given the speed with which they have been adopted in practice. Future research on their use should address these concerns through the development of expertise and best practices specific to public health, including a greater focus on user experience.

Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework

BACKGROUND

Conversational agents (CAs), also known as chatbots, are computer programs that simulate human conversations by using predetermined rule-based responses or artificial intelligence algorithms. They are increasingly used in health care, particularly via smartphones. There is, at present, no conceptual framework guiding the development of smartphone-based, rule-based CAs in health care. To fill this gap, we propose structured and tailored guidance for their design, development, evaluation, and implementation.

OBJECTIVE

The aim of this study was to develop a conceptual framework for the design, evaluation, and implementation of smartphone-delivered, rule-based, goal-oriented, and text-based CAs for health care.

METHODS

We followed the approach by Jabareen, which was based on the grounded theory method, to develop this conceptual framework. We performed 2 literature reviews focusing on health care CAs and conceptual frameworks for the development of mobile health interventions. We identified, named, categorized, integrated, and synthesized the information retrieved from the literature reviews to develop the conceptual framework. We then applied this framework by developing a CA and testing it in a feasibility study.

RESULTS

The Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER) conceptual framework includes 8 iterative steps grouped into 3 stages, as follows: design, comprising defining the goal, creating an identity, assembling the team, and selecting the delivery interface; development, including developing the content and building the conversation flow; and the evaluation and implementation of the CA. They were complemented by 2 cross-cutting considerations-user-centered design and privacy and security-that were relevant at all stages. This conceptual framework was successfully applied in the development of a CA to support lifestyle changes and prevent type 2 diabetes.

CONCLUSIONS

Drawing on published evidence, the DISCOVER conceptual framework provides a step-by-step guide for developing rule-based, smartphone-delivered CAs. Further evaluation of this framework in diverse health care areas and settings and for a variety of users is needed to demonstrate its validity. Future research should aim to explore the use of CAs to deliver health care interventions, including behavior change and potential privacy and safety concerns.

Emotionally Intelligent Chatbots: A Systematic Literature Review

Conversational technologies are transforming the landscape of human-machine interaction. Chatbots are increasingly being used in several domains to substitute human agents in performing tasks, answering questions, giving advice, and providing social and emotional support. Therefore, improving user satisfaction with these technologies is imperative for their successful integration. Researchers are leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to impart emotional intelligence capabilities in chatbots. This study provides a systematic review of research on developing emotionally intelligent chatbots. We employ a systematic approach to gather and analyze 42 articles published in the last decade. The review is aimed at providing a comprehensive analysis of past research to discover the problems addressed, the techniques used, and the evaluation measures employed by studies in embedding emotion in chatbot conversations. The study’s findings reveal that most studies are based on an open-domain generative chatbot architecture. Researchers mainly address the issue of accurately detecting the user’s emotion and generating emotionally relevant responses. Nearly 57% of the studies use an enhanced Seq2Seq encoding and decoding of the input of the conversational model. Almost all the studies use both the automatic and manual evaluation measures to evaluate the chatbots, with the BLEU measure being the most popular method for objective evaluation.

SlimMe, a Chatbot With Artificial Empathy for Personal Weight Management: System Design and Finding

As the obesity rate continues to increase persistently, there is an urgent need to develop an effective weight loss management strategy. Nowadays, the development of artificial intelligence (AI) and cognitive technologies coupled with the rapid spread of messaging platforms and mobile technology with easier access to internet technology offers professional dietitians an opportunity to provide extensive monitoring support to their clients through a chatbot with artificial empathy. This study aimed to design a chatbot with artificial empathic motivational support for weight loss called “SlimMe” and investigate how people react to a diet bot. The SlimMe infrastructure was built using Dialogflow as the natural language processing (NLP) platform and LINE mobile messenger as the messaging platform. We proposed a text-based emotion analysis to simulate artificial empathy responses to recognize the user's emotion. A preliminary evaluation was performed to investigate the early-stage user experience after a 7-day simulation trial. The result revealed that having an artificially empathic diet bot for weight loss management is a fun and exciting experience. The use of emoticons, stickers, and GIF images makes the chatbot response more interactive. Moreover, the motivational support and persuasive messaging features enable the bot to express more empathic and engaging responses to the user. In total, there were 1,007 bot responses from 892 user input messages. Of these, 67.38% (601/1,007) of the chatbot-generated responses were accurate to a relevant user request, 21.19% (189/1,007) inaccurate responses to a relevant request, and 10.31% (92/1,007) accurate responses to an irrelevant request. Only 1.12% (10/1,007) of the chatbot does not answer. We present the design of an artificially empathic diet bot as a friendly assistant to help users estimate their calorie intake and calories burned in a more interactive and engaging way. To our knowledge, this is the first chatbot designed with artificial empathy features, and it looks very promising in promoting long-term weight management. More user interactions and further data training and validation enhancement will improve the bot's in-built knowledge base and emotional intelligence base.

How Personality and Communication Patterns Affect Online ad-hoc Teams Under Pressure

Critical, time-bounded, and high-stress tasks, like incident response, have often been solved by teams that are cohesive, adaptable, and prepared. Although a fair share of the literature has explored the effect of personality on various other types of teams and tasks, little is known about how it contributes to teamwork when teams of strangers have to cooperate ad-hoc, fast, and efficiently. This study explores the dynamics between 120 crowd participants paired into 60 virtual dyads and their collaboration outcome during the execution of a high-pressure, time-bound task. Results show that the personality trait of Openness to experience may impact team performance with teams with higher minimum levels of Openness more likely to defuse the bomb on time. An analysis of communication patterns suggests that winners made more use of action and response statements. The team role was linked to the individual's preference of certain communication patterns and related to their perception of the collaboration quality. Highly agreeable individuals seemed to cope better with losing, and individuals in teams heterogeneous in Conscientiousness seemed to feel better about collaboration quality. Our results also suggest there may be some impact of gender on performance. As this study was exploratory in nature, follow-on studies are needed to confirm these results. We discuss how these findings can help the development of AI systems to aid the formation and support of crowdsourced remote emergency teams.

Therapeutic and technological advancements in haemophilia care: Quantum leaps forward

INTRODUCTION

Recent technological innovations in haemophilia have advanced at an astounding pace, including gene therapy programmes and bioengineered molecules for prophylaxis, products that reduce treatment burden through half-life extension, unique mechanisms of action, and subcutaneous administration. Additional technological advancements have emerged that are anticipated to further transform haemophilia care.

AIM

Review new and emerging haemophilia therapies, including replacement and bypassing products, digital applications, utilisation of big data, and personalised medicine.

METHODS

Data were obtained from peer-reviewed presentations/publications, and ongoing studies in haemophilia, ultrasonography, and artificial intelligence (AI).

RESULTS

Available treatments include new recombinant factors VIII (FVIII) and IX (FIX), extended half-life FVIII/IX products, a new FVIIa product for inhibitor patients, and a FVIIIa-mimetic. Several novel therapeutics are in clinical trials, including FVIIIa mimetics and inhibitors of naturally-occurring anticoagulants. Ongoing gene therapy trials suggest that a single vector infusion using an optimised construct can produce factor activity that reduces bleeding to near zero for years. Today, persons with haemophilia (PwH) approach a lifespan comparable to that of the general population, presenting treatment challenges for age-related co-morbidities. Technological innovations have broadened beyond therapeutics to include large database analyses utilising remote data collection with handheld devices, and to tailor AI applications. Current development efforts include patient-performed ultrasonography, algorithms for scan interpretation, and point-of-care haemostatic testing devices.

CONCLUSIONS

We have entered a golden age for haemophilia treatment and care with wide-ranging advancements targeting improved quality of life (QoL). Future-focused efforts by clinical and patient communities may provide equitable access and care for people impacted by haemophilia worldwide.

A Literature Survey of Recent Advances in Chatbots

Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation.

Educational chatbots for project-based learning: investigating learning outcomes for a team-based design course

Educational chatbots (ECs) are chatbots designed for pedagogical purposes and are viewed as an Internet of Things (IoT) interface that could revolutionize teaching and learning. These chatbots are strategized to provide personalized learning through the concept of a virtual assistant that replicates humanized conversation. Nevertheless, in the education paradigm, ECs are still novel with challenges in facilitating, deploying, designing, and integrating it as an effective pedagogical tool across multiple fields, and one such area is project-based learning. Therefore, the present study investigates how integrating ECs to facilitate team-based projects for a design course could influence learning outcomes. Based on a mixed-method quasi-experimental approach, ECs were found to improve learning performance and teamwork with a practical impact. Moreover, it was found that ECs facilitated collaboration among team members that indirectly influenced their ability to perform as a team. Nevertheless, affective-motivational learning outcomes such as perception of learning, need for cognition, motivation, and creative self-efficacy were not influenced by ECs. Henceforth, this study aims to add to the current body of knowledge on the design and development of EC by introducing a new collective design strategy and its pedagogical and practical implications.

Conversational Agents in Health Education: A Scoping Review Protocol (Preprint)

Background

Conversational agents have the ability to reach people through multiple mediums, including the online space, mobile phones, and hardware devices like Alexa and Google Home. Conversational agents provide an engaging method of interaction while making information easier to access. Their emergence into areas related to public health and health education is perhaps unsurprising. While the building of conversational agents is getting more simplified with time, there are still requirements of time and effort. There is also a lack of clarity and consistent terminology regarding what constitutes a conversational agent, how these agents are developed, and the kinds of resources that are needed to develop and sustain them. This lack of clarity creates a daunting task for those seeking to build conversational agents for health education initiatives.

Objective

This scoping review aims to identify literature that reports on the design and implementation of conversational agents to promote and educate the public on matters related to health. We will categorize conversational agents in health education in alignment with current classifications and terminology emerging from the marketplace. We will clearly define the variety levels of conversational agents, categorize currently existing agents within these levels, and describe the development models, tools, and resources being used to build conversational agents for health care education purposes.

Methods

This scoping review will be conducted by employing the Arksey and O’Malley framework. We will also be adhering to the enhancements and updates proposed by Levac et al and Peters et al. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews will guide the reporting of this scoping review. A systematic search for published and grey literature will be undertaken from the following databases: (1) PubMed, (2) PsychINFO, (3) Embase, (4) Web of Science, (5) SCOPUS, (6) CINAHL, (7) ERIC, (8) MEDLINE, and (9) Google Scholar. Data charting will be done using a structured format.

Results

Initial searches of the databases retrieved 1305 results. The results will be presented in the final scoping review in a narrative and illustrative manner.

Conclusions

This scoping review will report on conversational agents being used in health education today, and will include categorization of the levels of the agents and report on the kinds of tools, resources, and design and development methods used.

International Registered Report Identifier (IRRID)

DERR1-10.2196/31923

Monolingual and Cross-Lingual Intent Detection without Training Data in Target Languages

Due to recent DNN advancements, many NLP problems can be effectively solved using transformer-based models and supervised data. Unfortunately, such data is not available in some languages. This research is based on assumptions that (1) training data can be obtained by the machine translating it from another language; (2) there are cross-lingual solutions that work without the training data in the target language. Consequently, in this research, we use the English dataset and solve the intent detection problem for five target languages (German, French, Lithuanian, Latvian, and Portuguese). When seeking the most accurate solutions, we investigate BERT-based word and sentence transformers together with eager learning classifiers (CNN, BERT fine-tuning, FFNN) and lazy learning approach (Cosine similarity as the memory-based method). We offer and evaluate several strategies to overcome the data scarcity problem with machine translation, cross-lingual models, and a combination of the previous two. The experimental investigation revealed the robustness of sentence transformers under various cross-lingual conditions. The accuracy equal to ~0.842 is achieved with the English dataset with completely monolingual models is considered our top-line. However, cross-lingual approaches demonstrate similar accuracy levels reaching ~0.831, ~0.829, ~0.853, ~0.831, and ~0.813 on German, French, Lithuanian, Latvian, and Portuguese languages.