Socratic Artificial Intelligence Learning (SAIL): The Role of a Virtual Voice Assistant in Learning Orthopedic Knowledge.

OBJECTIVE: We hypothesized that learning through multiple sensory modalities would improve knowledge recall and recognition in orthopedic surgery residents and medical students. DESIGN: We developed a virtual study assistant, named Socratic Artificial Intelligence Learning (SAIL), based on a custom-built natural language processing algorithm. SAIL draws from practice questions approved by the American Board of Orthopaedic Surgery and quizzes users through a conversational, voice-enabled Web interface. We performed a randomized controlled study using a within-subjects, repeated measures design. SETTING: Participants first took a pretest to assess their baseline knowledge. They then underwent 10 days of spaced repetition training with practice questions using 3 modalities: oral response, typed response, and multiple-choice. Recall and recognition of the practiced knowledge were assessed via a post-test administered on the first day, first week, and 2 months after the training period. PARTICIPANTS: Twenty-four volunteers, who were medical students and orthopedic surgery residents at multiple US medical institutions. RESULTS: The oral, typed, and multiple-choice modalities produced similar recall and recognition rates. Although participants preferred using the traditional multiple-choice modality to study for standardized examinations, many were interested in supplementing their study routine with SAIL and believe that SAIL may improve their performance on written and oral examinations. CONCLUSIONS: SAIL is not inferior to the multiple-choice modality for learning orthopedic core knowledge. These results indicate that SAIL can be used to supplement traditional study methods. COMPETENCIES: medical knowledge; practice-based learning and improvement.

Open E-survey on the Use and Perception of Chatbots in Vascular Surgery.

Objective: Large language models and artificial intelligence (AI) based chatbots have brought new insights in healthcare, but they also raise major concerns. Their applications in vascular surgery have scarcely been investigated to date. This international survey aimed to evaluate the perceptions and feedback from vascular surgeons on the use of AI chatbots in vascular surgery. Methods: This international open e-survey comprised 50 items that covered participant characteristics, their perceptions on the use of AI chatbots in vascular surgery, and their user experience. The study was designed in accordance with the Checklist for reporting Results of Internet E-Surveys and was critically reviewed and approved by international members of the European Vascular Research Collaborative (EVRC) prior to distribution. Participation was open to self reported health professionals specialised (or specialising) in vascular surgery, including residents or fellows. Results: Of the 342 individuals who visited the survey page, 318 (93%) agreed to participate; 262 (82.4%) finished the survey and were included in the analysis. Most were consultants or attending physicians (64.1%), most declared not having any training or education related to AI in healthcare (221; 84.4%), and 198 (75.6%) rated their knowledge about the abilities of AI chatbots between average to very poor. Interestingly, 95 participants (36.3%) found that AI chatbots were very useful or somewhat useful in clinical practice at this stage and 229 (87.4%) agreed that they should be systematically validated prior to being used. Eighty participants (30.5%) had specifically tested it for questions related to clinical practice and 59 (73.8%) of them experienced issues or limitations. Conclusion: This international survey provides an overview of perceptions of AI chatbots by vascular surgeons and highlights the need to improve knowledge and training of health professionals to better evaluate, define, and implement their use in vascular surgery.

Exploring artificial intelligence role in improving service building engagement in sorting.

Waste management researchers have identified that the correct disposal of solid waste is better addressed upstream, where people properly sort their solid waste. Sorting solid waste is a practice that requires a behaviour friendly to sorting and willingness to continuously comply with waste management policies. However, the dynamic and ever-changing nature of service buildings' users makes fostering such behaviour challenging, potentially jeopardizing solid waste sorting efforts. Therefore, in this paper, we explore the possible role of artificial intelligence in alleviating the cumbersome process of sorting solid waste, by developing a virtual assistant that interacts with tenants via verbal and visual inputs to provide them with waste management services and instructions. The virtual assistant utilizes Natural Language Processing and computer vision techniques to enable voice and image recognition functionalities and achieved accuracy levels of 85% and 88% for verbal and visual inputs, respectively. The present work can be a solid foundation to investigate further implementation of virtual assistants to support sustainability practices in Facility Management.

Innovation and challenges of artificial intelligence technology in personalized healthcare.

As the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care. Through the introduction of innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, and automated appointment systems, AI is not only amplifying the quality of care but also empowering patients and fostering a more interactive dynamic between the patient and the healthcare provider. Yet, this progressive infiltration of AI into the healthcare sphere grapples with a plethora of challenges hitherto unseen. The exigent issues of data security and privacy, the specter of algorithmic bias, the requisite adaptability of regulatory frameworks, and the matter of patient acceptance and trust in AI solutions demand immediate and thoughtful resolution .The importance of establishing stringent and far-reaching policies, ensuring technological impartiality, and cultivating patient confidence is paramount to ensure that AI-driven enhancements in healthcare service provision remain both ethically sound and efficient. In conclusion, we advocate for an expansion of research efforts aimed at navigating the ethical complexities inherent to a technology-evolving landscape, catalyzing policy innovation, and devising AI applications that are not only clinically effective but also earn the trust of the patient populace. By melding expertise across disciplines, we stand at the threshold of an era wherein AI's role in healthcare is both ethically unimpeachable and conducive to elevating the global health quotient.

Longitudinal Coadaptation of Older Adults With Wearables and Voice-Activated Virtual Assistants: Scoping Review.

BACKGROUND: The integration of smart technologies, including wearables and voice-activated devices, is increasingly recognized for enhancing the independence and well-being of older adults. However, the long-term dynamics of their use and the coadaptation process with older adults remain poorly understood. This scoping review explores how interactions between older adults and smart technologies evolve over time to improve both user experience and technology utility. OBJECTIVE: This review synthesizes existing research on the coadaptation between older adults and smart technologies, focusing on longitudinal changes in use patterns, the effectiveness of technological adaptations, and the implications for future technology development and deployment to improve user experiences. METHODS: Following the Joanna Briggs Institute Reviewer's Manual and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, this scoping review examined peer-reviewed papers from databases including Ovid MEDLINE, Ovid Embase, PEDro, Ovid PsycINFO, and EBSCO CINAHL from the year 2000 to August 28, 2023, and included forward and backward searches. The search was updated on March 1, 2024. Empirical studies were included if they involved (1) individuals aged 55 years or older living independently and (2) focused on interactions and adaptations between older adults and wearables and voice-activated virtual assistants in interventions for a minimum period of 8 weeks. Data extraction was informed by the selection and optimization with compensation framework and the sex- and gender-based analysis plus theoretical framework and used a directed content analysis approach. RESULTS: The search yielded 16,143 papers. Following title and abstract screening and a full-text review, 5 papers met the inclusion criteria. Study populations were mostly female participants and aged 73-83 years from the United States and engaged with voice-activated virtual assistants accessed through smart speakers and wearables. Users frequently used simple commands related to music and weather, integrating devices into daily routines. However, communication barriers often led to frustration due to devices' inability to recognize cues or provide personalized responses. The findings suggest that while older adults can integrate smart technologies into their lives, a lack of customization and user-friendly interfaces hinder long-term adoption and satisfaction. The studies highlight the need for technology to be further developed so they can better meet this demographic's evolving needs and call for research addressing small sample sizes and limited diversity. CONCLUSIONS: Our findings highlight a critical need for continued research into the dynamic and reciprocal relationship between smart technologies and older adults over time. Future studies should focus on more diverse populations and extend monitoring periods to provide deeper insights into the coadaptation process. Insights gained from this review are vital for informing the development of more intuitive, user-centric smart technology solutions to better support the aging population in maintaining independence and enhancing their quality of life. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/51129.

Virtual Assistants' Response to Queries About Nicotine Replacement Therapy: A Mixed-Method Analysis.

This study focused on investigating the potential of Artificial Intelligent-powered Virtual Assistants (VAs) such as Amazon Alexa, Apple Siri, and Google Assistant as tools to help individuals seeking information about Nicotine Replacement Treatment (NRT) for smoking cessation. The researchers asked 40 NRT-related questions to each of the 3 VAs and evaluated the responses for voice recognition. The study used a cross-sectional mixed-method design with a total sample size of 360 responses. Inter-rater reliability and differences between VAs' responses were examined by SAS software, and qualitative assessments were conducted using NVivo software. Google Assistant achieved 100% voice recognition for NRT-related questions, followed by Apple Siri at 97.5%, and Amazon Alexa at 83.3%. Statistically significant differences were found between the responses of Amazon Alexa relative to both Google Assistant and Apple Siri. Researcher 1's ratings significantly differed from Researcher 2's (p = .001), but not from Researcher 3's (p = .11). Virtual Assistants occasionally struggled to understand the context or nuances of questions, lacked in-depth information in their responses, and provided generic or unrelated responses. Virtual Assistants have the potential to be incorporated into smoking cessation interventions and tobacco control initiatives, contingent upon improving their competencies.

A Virtual Assistant to Guide Early Postoperative Rehabilitation after Reverse Shoulder Arthroplasty: A Pilot Randomized Trial.

INTRODUCTION: Rehabilitation can improve outcomes after reverse shoulder arthroplasty (RSA). However, low adherence to rehabilitation and compliance rates are some of the main barriers. To address this public health issue, the goal of this research was to pilot test and evaluate the effectiveness of a chatbot to promote adherence to home rehabilitation in patients undergoing RSA. METHODS: A randomized pilot trial including patients undergoing RSA and early postoperative rehabilitation was performed. The control group received standard home rehabilitation; the experimental group received the same intervention supervised with a chatbot, with automated interactions that included messages to inform, motivate, and remember the days and exercises for 12 weeks. Compliance with rehabilitation and clinical measures of shoulder function, pain, and quality of life were assessed. RESULTS: 31 patients (17 experimental) with an average age of 70.4 (3.6) completed the intervention. Compliance was higher in the experimental group (77% vs. 65%; OR95% = 2.4 (0.5 to 11.4)). Statistically significant between-group differences with a CI of 95% were found in the QuickDASH questionnaire and self-reported quality of life. No differences were found in the rest of the measures. CONCLUSIONS: This pilot study suggests that the chatbot tool can be useful in promoting compliance with early postoperative home rehabilitation in patients undergoing RSA. Future randomized trials with adequate power are warranted to determine the clinical impact of the proposal.

Evaluation framework for conversational agents with artificial intelligence in health interventions: a systematic scoping review.

OBJECTIVES: Conversational agents (CAs) with emerging artificial intelligence present new opportunities to assist in health interventions but are difficult to evaluate, deterring their applications in the real world. We aimed to synthesize existing evidence and knowledge and outline an evaluation framework for CA interventions. MATERIALS AND METHODS: We conducted a systematic scoping review to investigate designs and outcome measures used in the studies that evaluated CAs for health interventions. We then nested the results into an overarching digital health framework proposed by the World Health Organization (WHO). RESULTS: The review included 81 studies evaluating CAs in experimental (n = 59), observational (n = 15) trials, and other research designs (n = 7). Most studies (n = 72, 89%) were published in the past 5 years. The proposed CA-evaluation framework includes 4 evaluation stages: (1) feasibility/usability, (2) efficacy, (3) effectiveness, and (4) implementation, aligning with WHO's stepwise evaluation strategy. Across these stages, this article presents the essential evidence of different study designs (n = 8), sample sizes, and main evaluation categories (n = 7) with subcategories (n = 40). The main evaluation categories included (1) functionality, (2) safety and information quality, (3) user experience, (4) clinical and health outcomes, (5) costs and cost benefits, (6) usage, adherence, and uptake, and (7) user characteristics for implementation research. Furthermore, the framework highlighted the essential evaluation areas (potential primary outcomes) and gaps across the evaluation stages. DISCUSSION AND CONCLUSION: This review presents a new framework with practical design details to support the evaluation of CA interventions in healthcare research. PROTOCOL REGISTRATION: The Open Science Framework (https://osf.io/9hq2v) on March 22, 2021.

Development of an Automated and Scalable Virtual Assistant to Aid in PPE Adherence: A Study with Implications for Applications within Anesthesiology.

Virtual assistants (VAs) are conversational agents that are able to provide cognitive aid. We developed a VA device for donning and doffing personal protective equipment (PPE) procedures and compared it to live human coaching to explore the feasibility of using VAs in the anesthesiology setting. An automated, scalable, voice-enabled VA was built using the Amazon Alexa device and Alexa Skills application. The device utilized voice-recognition technology to allow a touch-free interactive user experience. Audio and video step-by-step instructions for proper donning and doffing of PPE were programmed and displayed on an Echo Show device. The effectiveness of VA in aiding adherence to PPE protocols was compared to traditional human coaching in a randomized, controlled, single-blinded crossover design. 70 anesthesiologists, anesthesia assistants, respiratory therapists, and operating room nurses performed both donning and doffing procedures, once under step-by-step VA instructional guidance and once with human coaching. Performance was assessed using objective performance evaluation donning and doffing checklists. More participants in the VA group correctly performed the step of "Wash hands for 20 seconds" during both donning and doffing tests. Fewer participants in the VA group correctly performed the steps of "Put cap on and ensure covers hair and ears" and "Tie gown on back and around neck". The mean doffing total score was higher in the VA group; however, the donning score was similar in both groups. Our study demonstrates that it is feasible to use commercially available technology to create a voice-enabled VA that provides effective step-by-step instructions to healthcare professionals.

Effects of a Gamified Agent-Based System for Personalized Elderly Care: Pilot Usability Study.

BACKGROUND: The global percentage of older people has increased significantly over the last decades. Information and communication technologies have become essential to develop and motivate them to pursue healthier ways of living. This paper examines a personalized coaching health care service designed to maintain living conditions and active aging among older people. Among the technologies the service includes, we highlight the use of both gamification and cognitive assistant technologies designed to support older people and an application combining a cognitive virtual assistant to directly interact with the older person and provide feedback on their current health condition and several gamification techniques to motivate the older person to stay engaged with the application and pursuit of healthier daily habits. OBJECTIVE: This pilot study aimed to investigate the feasibility and usability of a gamified agent-based system for older people and obtain preliminary results on the effectiveness of the intervention regarding physical activity health outcomes. METHODS: The study was designed as an intervention study comparing pre- and posttest results. The proposed gamified agent-based system was used by 12 participants over 7 days (1 week), and step count data were collected with access to the Google Fit application programming interface. Step count data after the intervention were compared with average step count data before the intervention (average daily values over a period of 4 weeks before the intervention). A　1-tailed Student t test was used to determine the relationship between the dependent and independent variables. Usability was measured using the System Usability Scale questionnaire, which was answered by 8 of the 12 participants in the study. RESULTS: The posttest results showed significant pre- to posttest changes (P=.30; 1-tailed Student t test) with a moderate effect size (Cohen d=0.65). The application obtained an average usability score of 78. CONCLUSIONS: The presented pilot was validated, showing the positive health effects of using gamification techniques and a virtual cognitive assistant. Additionally, usability metrics considered for this study confirmed high adherence and interest from most participants in the pilot.

Evaluation of the Rosa Chatbot Providing Genetic Information to Patients at Risk of Hereditary Breast and Ovarian Cancer: Qualitative Interview Study.

BACKGROUND: Genetic testing has become an integrated part of health care for patients with breast or ovarian cancer, and the increasing demand for genetic testing is accompanied by an increasing need for easy access to reliable genetic information for patients. Therefore, we developed a chatbot app (Rosa) that is able to perform humanlike digital conversations about genetic BRCA testing. OBJECTIVE: Before implementing this new information service in daily clinical practice, we wanted to explore 2 aspects of chatbot use: the perceived utility and trust in chatbot technology among healthy patients at risk of hereditary cancer and how interaction with a chatbot regarding sensitive information about hereditary cancer influences patients. METHODS: Overall, 175 healthy individuals at risk of hereditary breast and ovarian cancer were invited to test the chatbot, Rosa, before and after genetic counseling. To secure a varied sample, participants were recruited from all cancer genetic clinics in Norway, and the selection was based on age, gender, and risk of having a BRCA pathogenic variant. Among the 34.9% (61/175) of participants who consented for individual interview, a selected subgroup (16/61, 26%) shared their experience through in-depth interviews via video. The semistructured interviews covered the following topics: usability, perceived usefulness, trust in the information received via the chatbot, how Rosa influenced the user, and thoughts about future use of digital tools in health care. The transcripts were analyzed using the stepwise-deductive inductive approach. RESULTS: The overall finding was that the chatbot was very welcomed by the participants. They appreciated the 24/7 availability wherever they were and the possibility to use it to prepare for genetic counseling and to repeat and ask questions about what had been said afterward. As Rosa was created by health care professionals, they also valued the information they received as being medically correct. Rosa was referred to as being better than Google because it provided specific and reliable answers to their questions. The findings were summed up in 3 concepts: "Anytime, anywhere"; "In addition, not instead"; and "Trustworthy and true." All participants (16/16) denied increased worry after reading about genetic testing and hereditary breast and ovarian cancer in Rosa. CONCLUSIONS: Our results indicate that a genetic information chatbot has the potential to contribute to easy access to uniform information for patients at risk of hereditary breast and ovarian cancer, regardless of geographical location. The 24/7 availability of quality-assured information, tailored to the specific situation, had a reassuring effect on our participants. It was consistent across concepts that Rosa was a tool for preparation and repetition; however, none of the participants (0/16) supported that Rosa could replace genetic counseling if hereditary cancer was confirmed. This indicates that a chatbot can be a well-suited digital companion to genetic counseling.

Health Information From Web Search Engines and Virtual Assistants About Pre-Exposure Prophylaxis for HIV Prevention in Adolescents and Young Adults: Content Analysis.

BACKGROUND: Adolescents and young adults are disproportionately affected by HIV, suggesting that HIV prevention methods such as pre-exposure prophylaxis (PrEP) should focus on this group as a priority. As digital natives, youth likely turn to internet resources regarding health topics they may not feel comfortable discussing with their medical providers. To optimize informed decision-making by adolescents and young adults most impacted by HIV, the information from internet searches should be educational, accurate, and readable. OBJECTIVE: The aims of this study were to compare the accuracy of web-based PrEP information found using web search engines and virtual assistants, and to assess the readability of the resulting information. METHODS: Adolescent HIV prevention clinical experts developed a list of 23 prevention-related questions that were posed to search engines (Ask.com, Bing, Google, and Yahoo) and virtual assistants (Amazon Alexa, Microsoft Cortana, Google Assistant, and Apple Siri). The first three results from search engines and virtual assistant web references, as well as virtual assistant verbal responses, were recorded and coded using a six-tier scale to assess the quality of information produced. The results were also entered in a web-based tool determining readability using the Flesch-Kincaid Grade Level scale. RESULTS: Google web search engine and Google Assistant more frequently produced PrEP information of higher quality than the other search engines and virtual assistants with scores ranging from 3.4 to 3.7 and 2.8 to 3.3, respectively. Additionally, the resulting information generally was presented in language at a seventh and 10th grade reading level according to the Flesch-Kincaid Grade Level scale. CONCLUSIONS: Adolescents and young adults are large consumers of technology and may experience discomfort discussing their sexual health with providers. It is important that efforts are made to ensure the information they receive about HIV prevention methods, and PrEP in particular, is comprehensive, comprehensible, and widely available.

Transforming Medical Education: Assessing the Integration of ChatGPT Into Faculty Workflows at a Caribbean Medical School.

INTRODUCTION: ChatGPT is a Large Language Model (LLM) which allows for natural language processing and interactions with users in a conversational style. Since its release in 2022, it has had a significant impact in many occupational fields, including medical education. We sought to gain insight into the extent and type of usage of ChatGPT at a Caribbean medical school, the American University of Antigua College of Medicine (AUA). METHODS: We administered a questionnaire to 87 full-time faculty at the school via email. We quantified and made graphical representations of the results via Qualtrics Experience Management software (QualtricsXM, Qualtrics, Provo, UT). Survey results were investigated using bar graph comparisons of absolute numbers and percentages for various categories related to ChatGPT usage, and descriptive statistics for Likert scale questions. RESULTS: We found an estimated 33% of faculty were currently using ChatGPT. There was broad acceptance of the program by those who were using it and most believed it should be an option for students. The primary task ChatGPT was being used for was multiple choice question (MCQ) generation. The primary concern faculty had was incorrect information being included in ChatGPT output. CONCLUSION: ChatGPT has been quickly adopted by a subset of the college faculty, demonstrating its growing acceptance. Given the level of approval expressed about the program, we believe ChatGPT will continue to form an important and expanding part of faculty workflows at AUA and in medical education in general.

Acceptance and Privacy Perceptions Toward Video-based Active and Assisted Living Technologies: Scoping Review.

BACKGROUND: The aging society posits new socioeconomic challenges to which a potential solution is active and assisted living (AAL) technologies. Visual-based sensing systems are technologically among the most advantageous forms of AAL technologies in providing health and social care; however, they come at the risk of violating rights to privacy. With the immersion of video-based technologies, privacy-preserving smart solutions are being developed; however, the user acceptance research about these developments is not yet being systematized. OBJECTIVE: With this scoping review, we aimed to gain an overview of existing studies examining the viewpoints of older adults and/or their caregivers on technology acceptance and privacy perceptions, specifically toward video-based AAL technology. METHODS: A total of 22 studies were identified with a primary focus on user acceptance and privacy attitudes during a literature search of major databases. Methodological quality assessment and thematic analysis of the selected studies were executed and principal findings are summarized. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines were followed at every step of this scoping review. RESULTS: Acceptance attitudes toward video-based AAL technologies are rather conditional, and are summarized into five main themes seen from the two end-user perspectives: caregiver and care receiver. With privacy being a major barrier to video-based AAL technologies, security and medical safety were identified as the major benefits across the studies. CONCLUSIONS: This review reveals a very low methodological quality of the empirical studies assessing user acceptance of video-based AAL technologies. We propose that more specific and more end user- and real life-targeting research is needed to assess the acceptance of proposed solutions.

The Effects of a Health Care Chatbot's Complexity and Persona on User Trust, Perceived Usability, and Effectiveness: Mixed Methods Study.

BACKGROUND: The rising adoption of telehealth provides new opportunities for more effective and equitable health care information mediums. The ability of chatbots to provide a conversational, personal, and comprehendible avenue for learning about health care information make them a promising tool for addressing health care inequity as health care trends continue toward web-based and remote processes. Although chatbots have been studied in the health care domain for their efficacy for smoking cessation, diet recommendation, and other assistive applications, few studies have examined how specific design characteristics influence the effectiveness of chatbots in providing health information. OBJECTIVE: Our objective was to investigate the influence of different design considerations on the effectiveness of an educational health care chatbot. METHODS: A 2×3 between-subjects study was performed with 2 independent variables: a chatbot's complexity of responses (eg, technical or nontechnical language) and the presented qualifications of the chatbot's persona (eg, doctor, nurse, or nursing student). Regression models were used to evaluate the impact of these variables on 3 outcome measures: effectiveness, usability, and trust. A qualitative transcript review was also done to review how participants engaged with the chatbot. RESULTS: Analysis of 71 participants found that participants who received technical language responses were significantly more likely to be in the high effectiveness group, which had higher improvements in test scores (odds ratio [OR] 2.73, 95% CI 1.05-7.41; P=.04). Participants with higher health literacy (OR 2.04, 95% CI 1.11-4.00, P=.03) were significantly more likely to trust the chatbot. The participants engaged with the chatbot in a variety of ways, with some taking a conversational approach and others treating the chatbot more like a search engine. CONCLUSIONS: Given their increasing popularity, it is vital that we consider how chatbots are designed and implemented. This study showed that factors such as chatbots' persona and language complexity are two design considerations that influence the ability of chatbots to successfully provide health care information.

Effects of an intelligent virtual assistant on office task performance and workload in a noisy environment.

This study examines the effects of noise and the use of an Intelligent Virtual Assistant (IVA) on the task performance and workload of office workers. Data were collected from forty-eight adults across varied office task scenarios (i.e., sending an email, setting up a timer/reminder, and searching for a phone number/address) and noise types (i.e., silence, non-verbal noise, and verbal noise). The baseline for this study is measured without the use of an IVA. Significant differences in performance and workload were found on both objective and subjective measures. In particular, verbal noise emerged as the primary factor affecting performance using an IVA. Task performance was dependent on the task scenario and noise type. Subjective ratings found that participants preferred to use IVA for less complex tasks. Future work can focus more on the effects of tasks, demographics, and learning curves. Furthermore, this work can help guide IVA system designers by highlighting factors affecting performance.

What do individuals with visual impairment need and want from a dialogue-based digital assistant?

CLINICAL SIGNIFICANCE: Optometrists are well-placed to provide helpful advice and guidance to patients with visual impairment but may not know how best to do this. The availability of a reliable and comprehensive conversational agent to which patients could be directed would be a valuable supplement to clinical intervention. BACKGROUND: The Artificial Intelligence in Visual Impairment (AIVI) Study is a proof-of-concept study to investigate whether ongoing information support for people with visual impairment (VI) can be provided by a dialogue-based digital assistant. The phase of the AIVI Study reported here explored the different dimensions of the information-seeking behaviour of individuals with VI: in particular, their need for information, the methods for obtaining it at present, and their views on the use of a digital assistant. METHODS: Qualitative data were collected from 120 UK-resident adults who responded to an online survey who were either visually impaired (86.7%), a carer or family member of someone with VI (5.8%), or a professional involved in the support of those with VI (7.5%). In addition, 10 in-depth 1:1 semi-structured interviews explored opinions in more detail. Thematic analysis was used to analyse the findings. RESULTS: Analysis of information needs identified 7 major themes: ocular condition; equipment, technology and adaptations; daily activities; registration; finance/employment; emotional support; and support for the carer. Participants used a wide variety of methods to access information from many sources and explained the barriers to access. Participants accepted the merit of a dialogue system aiding in a goal-directed search for specific information, but expressed reservations about its abilities in other areas, such as providing emotional support. CONCLUSIONS: Participants highlighted potential benefits, limitations, and requirements in using a digital assistant to access information about VI. These findings will inform the design of dialogue systems for populations with VI.

"Alexa, did the pandemic make you smarter?" A follow up content analysis of a virtual assistant's responses to a prenatal query.

To compare responses to 40 common prenatal questions from Amazon's virtual assistant, Alexa, one year apart during the COVID pandemic. Participants: Two researchers replicated a prenatal query using unique Alexa devices. A conceptual content analysis was conducted where the researchers independently queried Alexa the identical questions from their 2020 study during the same one-week timeframe, between May 20, 2021 and May 27, 2021. Alexa's responses were compared to the 2020 study and the American College of Obstetricians and Gynecologists data and verified by one of the researchers, a Certified Nurse Midwife. Alexa provided accurate responses to 26 (65%) of the questions, an increase by 55 percentage points from 2020. Alexa was able to recite the symptoms of COVID-19 illness but was unable to provide a response to the two other COVID-specific questions. Compared to the 2020 query, Alexa provided more reputable sources for the responses including the CDC, WHO, NIH, and Mayo Clinic. Alexa's ability to provide more accurate, evidence-based responses was remarkably improved in 2021. Mobile health tools, like Amazon Alexa, are highly utilized by the public, particularly with limited healthcare access during the COVID-19 pandemic. Technology-based platforms should provide credible, evidence-based content.

Smart Solutions to Keep Your Mental Balance.

Due to the coronavirus pandemic international conflicts, dramatic changes of daily living have been enforced, including new ways of providing patient assistance, based on artificial intelligence. The influence of these changes on people's mental health is still insufficiently analyzed and explored. Chatbots like Woebot, Wysa and Tess are gaining popularity, being attractive and easy to use. These achievements led us to develop a new application, being still in the testing phase, which has a positive impact on mental healthcare issues. It is a conversational system capable to diagnose people's negative, depressive, and anxious emotions during chatting, and to act as a psychological therapist and virtual friend. The proposed system, throughout the conversation, succeeds to decrease the patient's insecurity sentiments, by comforting their mood. In fact, an intelligent assistant for different mental health issues like stress, anxiety and depression, could become a very helpful information system.

Investigating Patients' Continuance Intention Toward Conversational Agents in Outpatient Departments: Cross-sectional Field Survey.

BACKGROUND: Conversational agents (CAs) have been developed in outpatient departments to improve physician-patient communication efficiency. As end users, patients' continuance intention is essential for the sustainable development of CAs. OBJECTIVE: The aim of this study was to facilitate the successful usage of CAs by identifying key factors influencing patients' continuance intention and proposing corresponding managerial implications. METHODS: This study proposed an extended expectation-confirmation model and empirically tested the model via a cross-sectional field survey. The questionnaire included demographic characteristics, multiple-item scales, and an optional open-ended question on patients' specific expectations for CAs. Partial least squares structural equation modeling was applied to assess the model and hypotheses. The qualitative data were analyzed via thematic analysis. RESULTS: A total of 172 completed questionaries were received, with a 100% (172/172) response rate. The proposed model explained 75.5% of the variance in continuance intention. Both satisfaction (ß=.68; P<.001) and perceived usefulness (ß=.221; P=.004) were significant predictors of continuance intention. Patients' extent of confirmation significantly and positively affected both perceived usefulness (ß=.817; P<.001) and satisfaction (ß=.61; P<.001). Contrary to expectations, perceived ease of use had no significant impact on perceived usefulness (ß=.048; P=.37), satisfaction (ß=-.004; P=.63), and continuance intention (ß=.026; P=.91). The following three themes were extracted from the 74 answers to the open-ended question: personalized interaction, effective utilization, and clear illustrations. CONCLUSIONS: This study identified key factors influencing patients' continuance intention toward CAs. Satisfaction and perceived usefulness were significant predictors of continuance intention (P<.001 and P<.004, respectively) and were significantly affected by patients' extent of confirmation (P<.001 and P<.001, respectively). Developing a better understanding of patients' continuance intention can help administrators figure out how to facilitate the effective implementation of CAs. Efforts should be made toward improving the aspects that patients reasonably expect CAs to have, which include personalized interactions, effective utilization, and clear illustrations.