Healthcare Chatbots Research Articles

The Utility and Limitations of Artificial Intelligence-Powered Chatbots in Healthcare

The Utility and Limitations of Artificial Intelligence-Powered Chatbots in Healthcare

Chatbots in medicine: certification process and applied use case.

Chatbots are computer programs designed to engage in natural language conversations in an easy and understandable way. Their use has been accelerated recently with the advent of large language models. However, their application in medicine and healthcare has been limited due to concerns over data privacy, the risk of providing medical diagnoses, and ensuring regulatory and legal compliance. Medicine and healthcare could benefit from chatbots if their scope is carefully defined and if they are used appropriately and monitored long-term. The confIAnce chatbot, developed at the Geneva University Hospitals and the University of Geneva, is an informational tool aimed at providing simplified information to the general public about primary care and chronic diseases. In this paper, we describe the certification and regulatory aspects applicable to chatbots in healthcare, particularly in primary care medicine. We use the confIAnce chatbot as a case study to explore the definition and classification of a medical device and its application to chatbots, considering the applicable Swiss regulations and the European Union AI Act. Chatbots can be classified anywhere from non-medical devices (informational tools that do not handle patient data or provide recommendations for treatment or diagnosis) to Class III medical devices (high-risk tools capable of predicting potentially fatal events and enabling a pre-emptive medical intervention). Key considerations in the definition and certification process include defining the chatbot's scope, ensuring compliance with regulations, maintaining security and safety, and continuously evaluating performance, risks, and utility. A lexicon of relevant terms related to artificial intelligence in healthcare, medical devices, and regulatory frameworks is also presented in this paper. Chatbots hold potential for both patients and healthcare professionals, provided that their scope of practice is clearly defined, and that they comply with regulatory requirements. This review aims to provide transparency by outlining the steps required for certification and regulatory compliance, making it valuable for healthcare professionals, scientists, developers, and patients.

ISETECH-AI-Powered Dental Consultation Chatbot: Enhancing Public Healthcare Accessibility and Awareness

The evolving landscape of healthcare has witnessed remarkable transformations owing to the integration of Artificial Intelligence (AI) and chatbot technology. This paper presents the development of an AI-powered dental consultation chatbot aimed at revolutionizing dental healthcare accessibility and awareness, particularly in the context of Malaysia. The chatbot serves as a valuable resource for dental self-diagnosis, information dissemination, and remote consultations, reducing the need for in-person visits to dental clinics, especially considering the COVID-19 pandemic. The adoption of chatbots in healthcare has gained momentum, driven by their ability to engage in natural language conversations, disseminate reliable information, and provide personalized assistance. Chatbots have emerged as essential tools in public health management, offering services ranging from disease understanding and information provision to counselling and facilitating doctor-patient communication. In the specific domain of dental healthcare, chatbots have the potential to alleviate challenges associated with iatrophobia (fear of disclosing dental problems) and the need for professional dental consultations. The objective of this work is to develop a user-friendly, AI-powered dental consultation chatbot accessible online. This chatbot allows users to engage in real-life conversations with the machine, conducting short self-diagnosis of their oral health conditions. Users can seek information about their dental issues, understand symptoms, and gain insights into their dental health before or after visiting a dentist. Furthermore, the chatbot system presented in this study holds the potential to ease the workload of dental practitioners by offering an alternative method of consultation. Modern digital dental technologies are increasingly focused on diagnosis and classification tasks, but few have explored the realm of dental consultation chatbots for public use. This project bridges this gap and extends the utility of AI-powered chatbots to the field of dental healthcare. This AI-powered dental consultation chatbot not only addresses the evolving healthcare needs of the post-pandemic era but also contributes to advancing dental healthcare accessibility and awareness among the population.

Bridging the health literacy gap through AI chatbot design: the impact of gender and doctor cues on chatbot trust and acceptance

PurposeNotwithstanding their potential benefits especially for individuals with low health literacy, users are still somewhat skeptical about the reliability of healthcare chatbots. The present study aims to address this challenge by investigating strategies to enhance users’ cognitive and emotional trust in healthcare chatbots. Particularly, this study aims to understand the effects of chatbot design cues in increasing trust and future chatbot use intention for low health literacy users.Design/methodology/approachWe conducted two experimental studies with a final sample of 327 (Study 1) and 241 (Study 2). Three different chatbots were developed (Chatbot design: Bot vs Male-doctor vs Female-doctor). Participants were asked to have a medical consultation with the chatbot. Participants self-reported their health literacy scores. The PROCESS model 7 was used to analyze the hypotheses.FindingsThe results showed that the female-doctor cues elicited greater cognitive and emotional trust, whereas the male-doctor cues only led to greater cognitive trust (vs bot-like cues). Importantly, this study found that users’ health literacy is a significant moderating factor in shaping cognitive and emotional trust. The results indicated that both the female and male-doctor cues’ positive effects on cognitive trust were significant for those with lower levels of health literacy. Furthermore, the positive effect of the female-doctor cues on emotional trust was also significant only for those whose health literacy level was low. The increased cognitive and emotional trust led to greater future intention to use the chatbot, confirming significant moderated mediation effects.Originality/valueDespite the strong economic and educational benefits of healthcare chatbots for low health literacy users, studies examining how healthcare chatbot design cues affect low health literate users surprisingly remained scarce. The results of this study suggest that healthcare chatbots can be a promising technological intervention to narrow the health literacy gap when aligned with appropriate design cues.

Transforming Health Care Through Chatbots for Medical History-Taking and Future Directions: Comprehensive Systematic Review.

The integration of artificial intelligence and chatbot technology in health care has attracted significant attention due to its potential to improve patient care and streamline history-taking. As artificial intelligence-driven conversational agents, chatbots offer the opportunity to revolutionize history-taking, necessitating a comprehensive examination of their impact on medical practice. This systematic review aims to assess the role, effectiveness, usability, and patient acceptance of chatbots in medical history-taking. It also examines potential challenges and future opportunities for integration into clinical practice. A systematic search included PubMed, Embase, MEDLINE (via Ovid), CENTRAL, Scopus, and Open Science and covered studies through July 2024. The inclusion and exclusion criteria for the studies reviewed were based on the PICOS (participants, interventions, comparators, outcomes, and study design) framework. The population included individuals using health care chatbots for medical history-taking. Interventions focused on chatbots designed to facilitate medical history-taking. The outcomes of interest were the feasibility, acceptance, and usability of chatbot-based medical history-taking. Studies not reporting on these outcomes were excluded. All study designs except conference papers were eligible for inclusion. Only English-language studies were considered. There were no specific restrictions on study duration. Key search terms included "chatbot*," "conversational agent*," "virtual assistant," "artificial intelligence chatbot," "medical history," and "history-taking." The quality of observational studies was classified using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria (eg, sample size, design, data collection, and follow-up). The RoB 2 (Risk of Bias) tool assessed areas and the levels of bias in randomized controlled trials (RCTs). The review included 15 observational studies and 3 RCTs and synthesized evidence from different medical fields and populations. Chatbots systematically collect information through targeted queries and data retrieval, improving patient engagement and satisfaction. The results show that chatbots have great potential for history-taking and that the efficiency and accessibility of the health care system can be improved by 24/7 automated data collection. Bias assessments revealed that of the 15 observational studies, 5 (33%) studies were of high quality, 5 (33%) studies were of moderate quality, and 5 (33%) studies were of low quality. Of the RCTs, 2 had a low risk of bias, while 1 had a high risk. This systematic review provides critical insights into the potential benefits and challenges of using chatbots for medical history-taking. The included studies showed that chatbots can increase patient engagement, streamline data collection, and improve health care decision-making. For effective integration into clinical practice, it is crucial to design user-friendly interfaces, ensure robust data security, and maintain empathetic patient-physician interactions. Future research should focus on refining chatbot algorithms, improving their emotional intelligence, and extending their application to different health care settings to realize their full potential in modern medicine. PROSPERO CRD42023410312; www.crd.york.ac.uk/prospero.

Integrating Chatbot Functionality in a Patient Summary Based Healthcare System.

The integration of chatbots in healthcare has gained attention due to their potential to enhance patient engagement and satisfaction. This paper presents a healthcare chatbot providing comprehensive access to patient summaries, aligned with the European Patient Summary. Leveraging Natural Language Processing (NLP) capabilities, our chatbot employs intent classification using the fine-tuned bioBERT model to categorize user queries effectively and extract relevant information from the patient summary stored in a database. We detail our methodology, which involves dataset creation, hyperparameter tuning, and model evaluation. Results demonstrate the effectiveness of our approach, with the trained model achieving high precision, recall, and F1 score across intent classes. Our study underscores the potential of emerging NLP techniques in patient interaction and healthcare delivery, covering the way for smarter, user-friendly companions.

The Effect of Healthcare Chatbots’ Information Presentation Styles on User Acceptance in a Knowledge Seeking Task

The rise of large language models offers new opportunities for disseminating more effective and equitable healthcare information. While past research has extensively investigated users’ perceptions of chatbots, few have examined the design strategies for chatbot information presentation. The objective of this research aims to explore how communication style (conversational or informative) and language style (technical or non-technical) affect user acceptance and ability performing a knowledge seeking task with a healthcare chatbot in a 2 × 2 between-subjects design. Following this, participants engaged in semi-structured interviews, where we analyzed participants’ experiences through inductive thematic analysis. Our findings indicate users generally found conversational chatbots to be more understandable with greater perceived interaction freedom than informative chatbots. These insights highlight the importance of aligning chatbot communication with users’ expectations. Future research should validate these findings with larger and more diverse populations, considering the impact of various chatbot interaction styles on user experience and long-term effectiveness.

Interacting with Healthcare Chatbot: Effects of Status Cues and Message Contingency on AI Credibility Assessment

How do status cues of AI agents alter human users’ interpretations and evaluations of these agents during human-AI interactions? Drawing upon social status theories and the CASA paradigm, this study explores the influences of status cues in AI-powered healthcare chatbots on AI credibility assessment via perceived AI agency and AI anxiety. The moderation effects of message contingency are also examined. A 2 (status cues: high-status vs. low-status) × 2 (message contingency: high vs. low) between-subjects experiment was conducted online (N = 209). Results revealed that healthcare chatbots with high-status (vs. low-status) cues were perceived as having more AI agency, which was further associated with higher perceived AI credibility. Perceived AI agency and AI anxiety sequentially mediated the effect of social status cues on perceived AI credibility. Results also showed that high (vs. low) message contingency significantly ameliorated users’ AI anxiety for high-status healthcare chatbots. Theoretical and practical implications are discussed.

AI Hesitancy and Acceptability-Perceptions of AI Chatbots for Chronic Health Management and Long COVID Support: Survey Study.

Artificial intelligence (AI) chatbots have the potential to assist individuals with chronic health conditions by providing tailored information, monitoring symptoms, and offering mental health support. Despite their potential benefits, research on public attitudes toward health care chatbots is still limited. To effectively support individuals with long-term health conditions like long COVID (or post-COVID-19 condition), it is crucial to understand their perspectives and preferences regarding the use of AI chatbots. This study has two main objectives: (1) provide insights into AI chatbot acceptance among people with chronic health conditions, particularly adults older than 55 years and (2) explore the perceptions of using AI chatbots for health self-management and long COVID support. A web-based survey study was conducted between January and March 2023, specifically targeting individuals with diabetes and other chronic conditions. This particular population was chosen due to their potential awareness and ability to self-manage their condition. The survey aimed to capture data at multiple intervals, taking into consideration the public launch of ChatGPT, which could have potentially impacted public opinions during the project timeline. The survey received 1310 clicks and garnered 900 responses, resulting in a total of 888 usable data points. Although past experience with chatbots (P<.001, 95% CI .110-.302) and online information seeking (P<.001, 95% CI .039-.084) are strong indicators of respondents' future adoption of health chatbots, they are in general skeptical or unsure about the use of AI chatbots for health care purposes. Less than one-third of the respondents (n=203, 30.1%) indicated that they were likely to use a health chatbot in the next 12 months if available. Most were uncertain about a chatbot's capability to provide accurate medical advice. However, people seemed more receptive to using voice-based chatbots for mental well-being, health data collection, and analysis. Half of the respondents with long COVID showed interest in using emotionally intelligent chatbots. AI hesitancy is not uniform across all health domains and user groups. Despite persistent AI hesitancy, there are promising opportunities for chatbots to offer support for chronic conditions in areas of lifestyle enhancement and mental well-being, potentially through voice-based user interfaces.

AI-enabled chatbots healthcare systems: an ethical perspective on trust and reliability

PurposeThe primary objective of this study is to investigate the ethical implications of deploying AI-enabled chatbots in the healthcare sector. In addition, the research underscores trust and reliability as critical factors in addressing the ethical challenges associated with these chatbots.Design/methodology/approachThis study takes a qualitative approach, conducting 13 semi-structured interviews with a diverse range of participants, including patients, healthcare professionals, academic researchers, ethicists, and legal experts. This broad spectrum of perspectives ensures a comprehensive understanding of the ethical implications of AI-enabled chatbots in healthcare. The rich exploratory data gathered from these interviews is then analysed using thematic analysis.FindingsThe findings of this study are highly significant in the context of AI-enabled healthcare chatbots. They highlight four major themes: developing trust, ensuring reliability, ethical considerations, and potential ethical implications. The interconnectedness of these themes forms a coherent narrative, highlighting the pivotal role of trust and reliability in mitigating ethical issues.Originality/valueThis study contributes to the existing literature on AI-enabled healthcare chatbots. It not only reveals potential ethical concerns associated with these technologies, such as data security, patient privacy, bias, and accountability, but it also places a significant emphasis on trust and reliability as critical elements that can boost user confidence and engagement in using AI-enabled chatbots for healthcare advice.

Health Care Chatbot

Health Care Chatbot

The Transformative Potential of AI Chatbots in Hearing Health Care

The Transformative Potential of AI Chatbots in Hearing Health Care

Development of AI-generated medical responses using the ChatGPT for cancer patients

Background and objectiveTo develop a healthcare chatbot service (AI-guided bot) that conducts real-time conversations using large language models to provide accurate health information to patients. MethodsTo provide accurate and specialized medical responses, we integrated several cancer practice guidelines. The size of the integrated meta-dataset was 1.17 million tokens. The integrated and classified metadata were extracted, transformed into text, segmented to specific character lengths, and vectorized using the embedding model. The AI-guide bot was implemented using Python 3.9. To enhance the scalability and incorporate the integrated dataset, we combined the AI-guide bot with OpenAI and the LangChain framework. To generate user-friendly conversations, a language model was developed based on Chat-Generative Pretrained Transformer (ChatGPT), an interactive conversational chatbot powered by GPT-3.5. The AI-guide bot was implemented using ChatGPT3.5 from Sep. 2023 to Jan. 2024. ResultsThe AI-guide bot allowed users to select their desired cancer type and language for conversational interactions. The AI-guided bot was designed to expand its capabilities to encompass multiple major cancer types. The performance of the AI-guide bot responses was 90.98 ± 4.02 (obtained by summing up the Likert scores). ConclusionsThe AI-guide bot can provide medical information quickly and accurately to patients with cancer who are concerned about their health.

Exploring the Relationships among Factors Influencing Healthcare Chatbot Adoption

Technology has revolutionized various industries; notably, chatbots impact healthcare through the efficient streamlining of medical procedures, cost reductions, and improved accessibility to healthcare services. Consequently, understanding how to promote the adoption of healthcare chatbots has become crucial for enhancing the healthcare industry and medical service quality. Despite numerous studies identifying factors influencing healthcare chatbot adoption, there is a notable lack of empirical verification regarding their interrelationships, leading to a significant knowledge gap. Therefore, this study aims to address this gap by developing a decision-making model to analyze the relationships among key factors regarding three dimensions: technology, user, and society. The model begins by filtering out insignificant factors based on experts’ opinions. Subsequently, it employs DEMATEL (Decision Making Trial and Evaluation Laboratory) to construct a causal relationships graph and the ISM (interpretive structural modeling) method to categorize these factors into a hierarchical order. To mitigate uncertainties stemming from the topic’s complexity, this study utilizes fuzzy sets and Z-number theory in the assessment process. The findings reveal a predominance of causal factors within the technological dimension. Notably, the quality of information provided by chatbots stands out as the most influential causal factor. The insights from this study suggest implications for both enterprises and governments to boost chatbot adoption in society.

A Survey of Artificial Intelligence Approaches for Mental Healthcare Chatbots

The research paper explores the evolving landscape of mental health care chatbots, examining their features, challenges, and future prospects. By synthesizing current research and developments, we aim to provide insights into the potential benefits and limitations of integrating chatbots into mental health care. According to the literature survey these systems have used different frameworks of Natural Language Processing (NLP), Natural Language Understanding (NLU) technologies and ML models. The chatbot aims to make a discussion between mortal and machine. Healthcare chatbot system stores the knowledge database to identify the judgment and making a decision to answer the question of the users and save their time and get the instant response. Keywords— Natural Language Processing, Artificial Intelligence, Mental Healthcare system, Machine learning, Natural Language Understanding, Chatbot

AI Health Chatbot using ML

This project aims to develop a personalized and interactive healthcare chatbot leveraging natural language processing and machine learning. It offers tailored advice based on user symptoms, medical history, and preferences. Integrated with healthcare databases, it provides reliable information and services like symptom analysis, triage recommendations, medication details, and personalized health tips. Seamlessly accessing patient records and appointment schedules within existing healthcare systems ensures a cohesive user experience. The AI healthcare chatbot optimizes services by reducing communication burdens, improving information accessibility, and enhancing patient engagement. Preliminary evaluations demonstrate promising results in user satisfaction and healthcare administration efficiency gains. Keywords: Healthcare chatbot, Symptom analysis, Disease Prediction, Medication information and Machine learning.

A systematic review of chatbots in inclusive healthcare: insights from the last 5 years

AbstractHealthcare is one of the most important sectors of our society, and during the COVID-19 pandemic a new challenge emerged—how to support people safely and effectively at home regarding their health-related problems. In this regard chatbots or conversational agents (CAs) play an increasingly important role, and are spreading rapidly. They can enhance not only user interaction by delivering quick feedback or responses, but also hospital management, thanks to several of their features. Considerable research is focused on making CAs more reliable, accurate, and robust. However, a critical aspect of chatbots is how to make them inclusive, in order to effectively support the interaction of users unfamiliar with technology, such as the elderly and people with disabilities. In this study, we investigate the current use of chatbots in healthcare, exploring their evolution over time and their inclusivity. The study was carried out on four digital libraries (ScienceDirect, IEEE Xplore, ACM Digital Library, and Google Scholar) on research articles published in the last 5 years, with a total of 21 articles describing chatbots implemented and actually used in the eHealth clinical area. The results showed a notable improvement in the use of chatbots in the last few years but also highlight some design issues, including poor attention to inclusion. Based on the findings, we recommend a different kind of approach for implementing chatbots with an inclusive accessibility-by-design approach.

HEALPAL: YOUR PERSONAL HEALTH COMAPANION

In an era where rapid technological advancements intersect with healthcare needs, the development of an intelligent healthcare chatbot has emerged as a promising solution. This study presents a comprehensive exploration into the conceptualization, design, and implementation of an AI-powered chatbot tailored to provide accurate and personalized healthcare information. Integrating Natural Language Processing (NLP) algorithms and Artificial Intelligence Markup Language (AIML), the chatbot aims to bridge the gap between individuals and reliable medical knowledge. Keywords—Chatbot, NLP, Markup Language

Achieving health equity through conversational AI: A roadmap for design and implementation of inclusive chatbots in healthcare.

The rapid evolution of conversational and generative artificial intelligence (AI) has led to the increased deployment of AI tools in healthcare settings. While these conversational AI tools promise efficiency and expanded access to healthcare services, there are growing concerns ethically, practically and in terms of inclusivity. This study aimed to identify activities which reduce bias in conversational AI and make their designs and implementation more equitable. A qualitative research approach was employed to develop an analytical framework based on the content analysis of 17 guidelines about AI use in clinical settings. A stakeholder consultation was subsequently conducted with a total of 33 ethnically diverse community members, AI designers, industry experts and relevant health professionals to further develop a roadmap for equitable design and implementation of conversational AI in healthcare. Framework analysis was conducted on the interview data. A 10-stage roadmap was developed to outline activities relevant to equitable conversational AI design and implementation phases: 1) Conception and planning, 2) Diversity and collaboration, 3) Preliminary research, 4) Co-production, 5) Safety measures, 6) Preliminary testing, 7) Healthcare integration, 8) Service evaluation and auditing, 9) Maintenance, and 10) Termination. We have made specific recommendations to increase conversational AI's equity as part of healthcare services. These emphasise the importance of a collaborative approach and the involvement of patient groups in navigating the rapid evolution of conversational AI technologies. Further research must assess the impact of recommended activities on chatbots' fairness and their ability to reduce health inequalities.

Towards more trusted virtual physicians: the combinative effects of healthcare chatbot design cues and threat perception on health information trust

ABSTRACT Although virtual healthcare assistants have been extensively adopted in the healthcare industry, users still question their reliability. Based on the Computers are Social Actor (CASA) paradigm, the current study conducted an experiment to examine how different chatbot design cues (Chatbot vs. Layperson vs. Doctor) affect users’ trust towards virtual healthcare assistants. Results indicate that the doctor-like design cues and bot-like design cues (vs. layperson-like design cues) elicited significantly greater perceived chatbot expertise, which enhanced users’ trust in health information. This study further found the significant moderating effects of users’ perceived threat on chatbot expertise and privacy concerns. Positive effects of the doctor-like design cue and the bot-like design cue on perceived expertise were found to be significant only for those whose perceived threat was high. Interestingly, the doctor-like design cues led to greater (less) privacy concerns when the perceived threat level was low (high). The findings provide human–computer interaction researchers and chatbot UX/UI designers with important theoretical and practical implications.