1
|
Lin SJ, Sun CY, Chen DN, Kang YN, Lai NM, Chen KH, Chen C. Perioperative application of chatbots: a systematic review and meta-analysis. BMJ Health Care Inform 2024; 31:e100985. [PMID: 39032946 PMCID: PMC11261686 DOI: 10.1136/bmjhci-2023-100985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/02/2024] [Indexed: 07/23/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Patient-clinician communication and shared decision-making face challenges in the perioperative period. Chatbots have emerged as valuable support tools in perioperative care. A simultaneous and complete comparison of overall benefits and harm of chatbot application is conducted. MATERIALS MEDLINE, EMBASE and the Cochrane Library were systematically searched for studies published before May 2023 on the benefits and harm of chatbots used in the perioperative period. The major outcomes assessed were patient satisfaction and knowledge acquisition. Untransformed proportion (PR) with a 95% CI was used for the analysis of continuous data. Risk of bias was assessed using the Cochrane Risk of Bias assessment tool version 2 and the Methodological Index for Non-Randomised Studies. RESULTS Eight trials comprising 1073 adults from four countries were included. Most interventions (n = 5, 62.5%) targeted perioperative care in orthopaedics. Most interventions use rule-based chatbots (n = 7, 87.5%). This meta-analysis found that the majority of the participants were satisfied with the use of chatbots (mean proportion=0.73; 95% CI: 0.62 to 0.85), and agreed that they gained knowledge in their perioperative period (mean proportion=0.80; 95% CI: 0.74 to 0.87). CONCLUSION This review demonstrates that perioperative chatbots are well received by the majority of patients with no reports of harm to-date. Chatbots may be considered as an aid in perioperative communication between patients and clinicians and shared decision-making. These findings may be used to guide the healthcare providers, policymakers and researchers for enhancing perioperative care.
Collapse
Affiliation(s)
- Shih-Jung Lin
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chin-Yu Sun
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan
| | - Dan-Ni Chen
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan
- Executive Master of Business Administration Program, College of Business, University of Texas at Arlington, Arlington, Texas, USA
| | - Yi-No Kang
- Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan
- Evidence-Based Medicine Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Nai Ming Lai
- Digital Health and Innovation Impact Lab, Taylor's University, Subang Jaya, Malaysia
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Malaysia
| | - Kee-Hsin Chen
- Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan
- Post-Baccalaureate Program in Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan
- Research Center in Nursing Clinical Practice, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Nursing, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Evidence-Based Knowledge Translation Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Visiting Associate Professor, School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Selangor 47500, Malaysia
| | - Chiehfeng Chen
- Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan
- Evidence-Based Medicine Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Division of Plastic Surgery, Department of Surgery, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| |
Collapse
|
2
|
Kim M. Unveiling the e-Servicescape of ChatGPT: Exploring User Psychology and Engagement in AI-Powered Chatbot Experiences. Behav Sci (Basel) 2024; 14:558. [PMID: 39062382 PMCID: PMC11273378 DOI: 10.3390/bs14070558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/29/2024] [Accepted: 06/30/2024] [Indexed: 07/28/2024] Open
Abstract
This study explores the psychological motivations that drive ChatGPT users to embrace and sustain the use of such technology based on the fundamental notion of the environmental psychology theory, including servicescapes. To do so, this study delves into the influence of ChatGPT's e-servicescapes on users' emotional states and intention to engage with ChatGPT for decision-making processes. This study conducted an online survey among ChatGPT users in the United States. Structural equation modeling revealed that negative emotions were significantly influenced by various e-servicescape sub-dimensions, including security, visual appeal, entertainment value, originality of design, and social factors. Positive emotions, on the other hand, were influenced by factors such as visual appeal, customization, interactivity, and relevance of information. Both positive and negative emotions significantly affected user satisfaction, which, in turn, shaped their behavioral intention to engage with ChatGPT. This study contributes to the understanding of digital environmental psychology and chatbots by extending the notion of e-servicescapes to the context of AI-based services. It underscores the significance of e-servicescapes in shaping user experiences and provides valuable insights for business scholars and marketing practitioners.
Collapse
Affiliation(s)
- Minseong Kim
- Department of Management & Marketing, College of Business, Louisiana State University Shreveport, Shreveport, LA 71115, USA
| |
Collapse
|
3
|
Yaghy A, Yaghy M, Shields JA, Shields CL. Large Language Models in Ophthalmology: Potential and Pitfalls. Semin Ophthalmol 2024; 39:289-293. [PMID: 38179986 DOI: 10.1080/08820538.2023.2300808] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/06/2023] [Indexed: 01/06/2024]
Abstract
Large language models (LLMs) show great promise in assisting clinicians in general, and ophthalmology in particular, through knowledge synthesis, decision support, accelerating research, enhancing education, and improving patient interactions. Specifically, LLMs can rapidly summarize the latest literature to keep clinicians up-to-date. They can also analyze patient data to highlight crucial insights and recommend appropriate tests or referrals. LLMs can automate tedious research tasks like data cleaning and literature reviews. As AI tutors, LLMs can fill knowledge gaps and assess competency in trainees. As chatbots, they can provide empathetic, personalized responses to patient inquiries and improve satisfaction. The visual capabilities of LLMs like GPT-4 allow assisting the visually impaired by describing environments. However, there are significant ethical, technical, and legal challenges around the use of LLMs that should be addressed regarding privacy, fairness, robustness, attribution, and regulation. Ongoing oversight and refinement of models is critical to realize benefits while minimizing risks and upholding responsible AI principles. If carefully implemented, LLMs hold immense potential to push the boundaries of care, discovery, and quality of life for ophthalmology patients.
Collapse
Affiliation(s)
- Antonio Yaghy
- Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USA
| | - Maria Yaghy
- Pediatric Emergency and Infectious Disease, Centre Hospitalier Universitaire Timone Enfants, Marseille, France
| | - Jerry A Shields
- Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USA
| | - Carol L Shields
- Ocular Oncology Service, Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USA
| |
Collapse
|
4
|
Passanante A, Pertwee E, Lin L, Lee KY, Wu JT, Larson HJ. Conversational AI and Vaccine Communication: Systematic Review of the Evidence. J Med Internet Res 2023; 25:e42758. [PMID: 37788057 PMCID: PMC10582806 DOI: 10.2196/42758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 05/09/2023] [Accepted: 07/31/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND Since the mid-2010s, use of conversational artificial intelligence (AI; chatbots) in health care has expanded significantly, especially in the context of increased burdens on health systems and restrictions on in-person consultations with health care providers during the COVID-19 pandemic. One emerging use for conversational AI is to capture evolving questions and communicate information about vaccines and vaccination. OBJECTIVE The objective of this systematic review was to examine documented uses and evidence on the effectiveness of conversational AI for vaccine communication. METHODS This systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, Web of Science, PsycINFO, MEDLINE, Scopus, CINAHL Complete, Cochrane Library, Embase, Epistemonikos, Global Health, Global Index Medicus, Academic Search Complete, and the University of London library database were searched for papers on the use of conversational AI for vaccine communication. The inclusion criteria were studies that included (1) documented instances of conversational AI being used for the purpose of vaccine communication and (2) evaluation data on the impact and effectiveness of the intervention. RESULTS After duplicates were removed, the review identified 496 unique records, which were then screened by title and abstract, of which 38 were identified for full-text review. Seven fit the inclusion criteria and were assessed and summarized in the findings of this review. Overall, vaccine chatbots deployed to date have been relatively simple in their design and have mainly been used to provide factual information to users in response to their questions about vaccines. Additionally, chatbots have been used for vaccination scheduling, appointment reminders, debunking misinformation, and, in some cases, for vaccine counseling and persuasion. Available evidence suggests that chatbots can have a positive effect on vaccine attitudes; however, studies were typically exploratory in nature, and some lacked a control group or had very small sample sizes. CONCLUSIONS The review found evidence of potential benefits from conversational AI for vaccine communication. Factors that may contribute to the effectiveness of vaccine chatbots include their ability to provide credible and personalized information in real time, the familiarity and accessibility of the chatbot platform, and the extent to which interactions with the chatbot feel "natural" to users. However, evaluations have focused on the short-term, direct effects of chatbots on their users. The potential longer-term and societal impacts of conversational AI have yet to be analyzed. In addition, existing studies do not adequately address how ethics apply in the field of conversational AI around vaccines. In a context where further digitalization of vaccine communication can be anticipated, additional high-quality research will be required across all these areas.
Collapse
Affiliation(s)
- Aly Passanante
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Ed Pertwee
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Leesa Lin
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong, China (Hong Kong)
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Kristi Yoonsup Lee
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong, China (Hong Kong)
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Joseph T Wu
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong, China (Hong Kong)
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Heidi J Larson
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States
| |
Collapse
|
5
|
Nadarzynski T, Lunt A, Knights N, Bayley J, Llewellyn C. "But can chatbots understand sex?" Attitudes towards artificial intelligence chatbots amongst sexual and reproductive health professionals: An exploratory mixed-methods study. Int J STD AIDS 2023; 34:809-816. [PMID: 37269292 PMCID: PMC10561522 DOI: 10.1177/09564624231180777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 05/22/2023] [Indexed: 06/05/2023]
Abstract
BACKGROUND Artificial Intelligence (AI)-enabled chatbots can offer anonymous education about sexual and reproductive health (SRH). Understanding chatbot acceptability and feasibility allows the identification of barriers to the design and implementation. METHODS In 2020, we conducted an online survey and qualitative interviews with SRH professionals recruited online to explore the views on AI, automation and chatbots. Qualitative data were analysed thematically. RESULTS Amongst 150 respondents (48% specialist doctor/consultant), only 22% perceived chatbots as effective and 24% saw them as ineffective for SRH advice [Mean = 2.91, SD = 0.98, range: 1-5]. Overall, there were mixed attitudes towards SRH chatbots [Mean = 4.03, SD = 0.87, range: 1-7]. Chatbots were most acceptable for appointment booking, general sexual health advice and signposting, but not acceptable for safeguarding, virtual diagnosis, and emotional support. Three themes were identified: "Moving towards a 'digital' age'", "AI improving access and service efficacy", and "Hesitancy towards AI". CONCLUSIONS Half of SRH professionals were hesitant about the use of chatbots in SRH services, attributed to concerns about patient safety, and lack of familiarity with this technology. Future studies should explore the role of AI chatbots as supplementary tools for SRH promotion. Chatbot designers need to address the concerns of health professionals to increase acceptability and engagement with AI-enabled services.
Collapse
Affiliation(s)
| | - Alexandria Lunt
- Brighton and Sussex Medical School, University of Sussex, Brighton
| | | | | | - Carrie Llewellyn
- Brighton and Sussex Medical School, University of Sussex, Brighton
| |
Collapse
|
6
|
Chow JCL, Wong V, Sanders L, Li K. Developing an AI-Assisted Educational Chatbot for Radiotherapy Using the IBM Watson Assistant Platform. Healthcare (Basel) 2023; 11:2417. [PMID: 37685452 PMCID: PMC10487627 DOI: 10.3390/healthcare11172417] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 09/10/2023] Open
Abstract
Objectives: This study aims to make radiotherapy knowledge regarding healthcare accessible to the general public by developing an AI-powered chatbot. The interactive nature of the chatbot is expected to facilitate better understanding of information on radiotherapy through communication with users. Methods: Using the IBM Watson Assistant platform on IBM Cloud, the chatbot was constructed following a pre-designed flowchart that outlines the conversation flow. This approach ensured the development of the chatbot with a clear mindset and allowed for effective tracking of the conversation. The chatbot is equipped to furnish users with information and quizzes on radiotherapy to assess their understanding of the subject. Results: By adopting a question-and-answer approach, the chatbot can engage in human-like communication with users seeking information about radiotherapy. As some users may feel anxious and struggle to articulate their queries, the chatbot is designed to be user-friendly and reassuring, providing a list of questions for the user to choose from. Feedback on the chatbot's content was mostly positive, despite a few limitations. The chatbot performed well and successfully conveyed knowledge as intended. Conclusions: There is a need to enhance the chatbot's conversation approach to improve user interaction. Including translation capabilities to cater to individuals with different first languages would also be advantageous. Lastly, the newly launched ChatGPT could potentially be developed into a medical chatbot to facilitate knowledge transfer.
Collapse
Affiliation(s)
- James C. L. Chow
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1X6, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Valerie Wong
- Department of Physics, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada;
| | - Leslie Sanders
- Department of Humanities, York University, Toronto, ON M3J 1P3, Canada;
| | - Kay Li
- Department of English, University of Toronto, Toronto, ON M5R 2M8, Canada;
| |
Collapse
|
7
|
Černý M. Educational Psychology Aspects of Learning with Chatbots without Artificial Intelligence: Suggestions for Designers. Eur J Investig Health Psychol Educ 2023; 13:284-305. [PMID: 36826206 PMCID: PMC9955713 DOI: 10.3390/ejihpe13020022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/22/2023] [Accepted: 01/25/2023] [Indexed: 02/03/2023] Open
Abstract
Chatbots without artificial intelligence can play the role of practical and easy-to-implement learning objects in e-learning environments, allowing a reduction in social or psychological isolation. This research, with a sample of 79 students, explores the principles that need to be followed in designing this kind of chatbot in education in order to ensure an acceptable outcome for students. Research has shown that students interacting with a chatbot without artificial intelligence expect similar psychological and communicative responses to those of a live human, project the characteristics of the chatbot from the dialogue, and are taken aback when the chatbot does not understand or cannot help them sufficiently. The study is based on a design through research approach, in which students in information studies and library science interacted with a specific chatbot focused on information retrieval, and recorded their experiences and feelings in an online questionnaire. The study intends to find principles for the design of chatbots without artificial intelligence so that students feel comfortable interacting with them.
Collapse
Affiliation(s)
- Michal Černý
- Faculty of Art, Masaryk University in Brno, 602 00 Brno, Czech Republic
| |
Collapse
|
8
|
Wu Y, Kuru O, Kim DH, Kim S. COVID-19 News Exposure and Vaccinations: A Moderated Mediation of Digital News Literacy Behavior and Vaccine Misperceptions. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:891. [PMID: 36613213 PMCID: PMC9820342 DOI: 10.3390/ijerph20010891] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Being exposed to and believing in misinformation about COVID-19 vaccines is a challenge for vaccine acceptance. Yet, how countervailing factors such as news literacy could complicate "the information exposure-belief in vaccine misinformation-vaccination" path needs to be unpacked to understand the communication of scientific information about COVID-19. This study examines (1) the mediating role of belief in vaccine misinformation between COVID-19 information exposure and vaccination behavior and (2) the moderating role of news literacy behaviors. We examine these relationships by collecting data in two distinct societies: the United States and South Korea. We conducted online surveys in June and September 2021 respectively for each country (N = 1336 [the U.S.]; N = 550 [South Korea]). Our results showed a significant moderated mediation model, in which the association between digital media reliance and COVID-19 vaccination was mediated through vaccine misperceptions, and the relationship between digital media reliance and misinformed belief was further moderated by news literacy behavior. Unexpectedly, we found that individuals with stronger news literacy behavior were more susceptible to misinformation belief. This study contributes to the extant literature on the communication of COVID-19 science through probing into the mediating role of belief in vaccine-related misinformation and the moderating role of news literacy behavior in relation to COVID-19 information exposure and vaccination behaviors. It also reflects the concept of news literacy behavior and discusses how it could be further refined to exert its positive impact in correcting misinformation beliefs.
Collapse
Affiliation(s)
- Yuanyuan Wu
- Department of Communications and New Media, Faculty of Arts & Social Sciences, National University of Singapore, Singapore 117416, Singapore
| | - Ozan Kuru
- Department of Communications and New Media, Faculty of Arts & Social Sciences, National University of Singapore, Singapore 117416, Singapore
| | - Dam Hee Kim
- Department of Communication, College of Social & Behavioral Sciences, The University of Arizona, Tucson, AZ 85721, USA
| | - Seongcheol Kim
- School of Media and Communication, Korea University, Seoul 02841, Republic of Korea
| |
Collapse
|
9
|
Zhou Q, Li B, Han L, Jou M. Talking to a bot or a wall? How chatbots vs. human agents affect anticipated communication quality. COMPUTERS IN HUMAN BEHAVIOR 2023. [DOI: 10.1016/j.chb.2023.107674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
|
10
|
De Keyser A, Kunz WH. Living and working with service robots: a TCCM analysis and considerations for future research. JOURNAL OF SERVICE MANAGEMENT 2022. [DOI: 10.1108/josm-12-2021-0488] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
PurposeService robots are now an integral part of people's living and working environment, making service robots one of the hot topics for service researchers today. Against that background, the paper reviews the recent service robot literature following a Theory-Context-Characteristics-Methodology (TCCM) approach to capture the state of art of the field. In addition, building on qualitative input from researchers who are active in this field, the authors highlight where opportunities for further development and growth lie.Design/methodology/approachThe paper identifies and analyzes 88 manuscripts (featuring 173 individual studies) published in academic journals featured on the SERVSIG literature alert. In addition, qualitative input gathered from 79 researchers who are active in the service field and doing research on service robots is infused throughout the manuscript.FindingsThe key research foci of the service robot literature to date include comparing service robots with humans, the role of service robots' look and feel, consumer attitudes toward service robots and the role of service robot conversational skills and behaviors. From a TCCM view, the authors discern dominant theories (anthropomorphism theory), contexts (retail/healthcare, USA samples, Business-to-Consumer (B2C) settings and customer focused), study characteristics (robot types: chatbots, not embodied and text/voice-based; outcome focus: customer intentions) and methodologies (experimental, picture-based scenarios).Originality/valueThe current paper is the first to analyze the service robot literature from a TCCM perspective. Doing so, the study gives (1) a comprehensive picture of the field to date and (2) highlights key pathways to inspire future work.
Collapse
|