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  • Systematic Review
  • Open Access

29 March 2022

A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions

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1
School of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, Australia
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School of Communication, Faculty of Arts and Social Sciences, University of Technology Sydney, Sydney 2007, Australia
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School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney 2007, Australia
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School of Biomedical Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, Australia
Sensors2022, 22(7), 2625;https://doi.org/10.3390/s22072625
This article belongs to the Special Issue Advanced Machine Learning Techniques for Biomedical Imaging Sensing and Healthcare Applications
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Abstract

This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.

1. Introduction

The availability and use of conversational agents have been increasing due to advances in technologies such as natural language processing (NLP), voice recognition, and artificial intelligence (AI). Conversational agents (CAs), also known as chatbots or dialogue systems, are computer systems that communicate with users through natural language user interfaces involving images, text, and voice [1,2]. Google Assistance, Apple Siri, Amazon Alexa, and Microsoft Cortana are common CAs with voice-activated interfaces. In the last decade, CAs’ popularity has increased, particularly those that use unconstrained natural language [3,4,5]. For example, consumers can talk to CAs on their smartphones for daily tasks, such as managing their calendars and retrieving information [6,7].
Recently, AI-based CAs have demonstrated multiple benefits in many domains, especially in healthcare. It is used to deliver scalable, less costly medical support solutions that can help at any time via smartphone apps or online [8,9]. For example, support and follow-up for adults after cancer treatment via chatbot reduced the patients’ anxiety without needing a psychiatrist [10,11,12]. Hence, CAs can play an useful role in health care, improving consultations by assisting clinicians and patients, supporting consumers with behavior change, and assisting older people in their living environments [13,14,15]. They can also help in completing specific tasks such as self-monitoring and overcoming obstacles for self-management, which is important in chronic disease management and in the fight against pandemics [6,16].
Chronic conditions and mental health conditions are increasing worldwide. Chronic diseases are one of the biggest healthcare challenges of the 21st century [17,18]. Chronic conditions are “characterized by their long-lasting and persistent effects. Once present, they often persist throughout a person’s life, so there is generally a need for long-term management by individuals and health professionals” [1]. Additionally, chronic conditions reduce one’s quality of life and increase healthcare expenses through disability, repeated hospitalization, and treatment procedures. According to the World Health Organization statistics of 2020, non-communicable diseases (e.g., hypertension, diabetes, and depression) and suicide are still prevalent reasons for death in 2016 [19]. In the US, about 60% of adults have chronic diseases, causing the annual health care expenditure approximately 86.2% of the $2.6 trillion [20]. In 2018, the Australian Institute of Health and Welfare claims that diabetes is one of Australia’s eight common chronic conditions, contributing to 61% of the disease burden, 37% of hospitalizations, and 87% of deaths [21]. There are about 1.13 billion people who had suffered from hypertension in 2015, and the number is still increasing. About 46% of adults do not know that they have hypertension. All statistics about chronic conditions show how serious they are and their effect on people’s lives [19].
Some research studies have shown advantages from the use of AI-enabled CAs in different healthcare settings, such as enabling behavior change, coaching to support a healthy lifestyle, helping breast cancer patients, and self-anamnesis for therapy patients [7,22,23]. Prior systematic literature reviews explored a variety of CAs in general health care [1,6,24] and aspects of the personalization of health care chatbots using AI [25]. However, there is little evidence on the use of AI-based CAs in chronic disease health care. This paper aims to address the gap by reviewing different kinds of CAs used in health care for chronic conditions, different types of communication technology, evaluation measures of CAs, and AI methods used.
Section 2 presents methods explaining the search strategy, eligibility criteria, screening, and data extraction processes. Section 3 addresses the results that include descriptions of included studies, CAs, AI methods, and evaluation measures. Section 4 provides a discussion of findings and outcomes. Section 5 presents the conclusion and future work.

2. Methods

  • Reporting standards
A systematic literature review has been performed which followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist [24]. The review protocol in Appendix A was registered on OSF Preregistration, with DOI 10.17605/OSF.IO/GDWSH.
  • Search strategy
A systematic search was performed in February 2021, on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library, not restricted by year or language. Search terms included “conversational agents”, “dialogue systems”, “relational agents”, and “chatbots” (complete search strategy available in Appendix A) [1,6,25,26]. Gray literature that was also identified in those databases (including conference proceedings, theses, dissertations), were included for screening.
  • Study selection criteria
The criteria included primary research studies that focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases using CAs, and tested the system with human users. Reviews, perspectives, opinion papers, or news articles were excluded based on exclusion criteria. In addition, studies that reported on evaluations based on human users interacting with the entire health system were excluded. The studies that evaluated only individual components of natural language understanding and CAs’ automatic speech recognition, dialogue management, response generation, and text-to-speech synthesis were excluded. The last exclusion criteria were studies using “Wizard of Oz” methods, where dialogue generated by a human operator rather than the CAs, were excluded [1,6,9].
  • Screening, data extraction, and synthesis
All references identified through the searches were downloaded. Then, duplicates were eliminated using reference managers (Endnote and Mendeley). Next, the titles and abstracts for each paper were exported from the reference manager into an Excel spreadsheet.
Before starting the screening process, the procedures of the screening were handled. After that, the first filter used was a screening filter based on the information contained in their titles and abstracts. Two independent reviewers conducted this screening. Two independent reviewers also conducted the full-text screening. The exclusion of an article was resolved with a Zoom meeting between two independent reviewers. Four reviewers extracted the following data for each study: first author, year of publication, study location, chronic condition, study aim, study types and methods, participants’ characteristics, evaluation measures, and main findings (Table 1). Evaluation measures were extracted based on three types: technical performance, user experience, and health-related measures. The technical performance of CAs was considered an objective assessment of the technical properties of the whole system. The technical performance measure is not included in Table 1 because most papers had not reported it, so the information about this measure will be in the next sections. User experience evaluation included the subjective assessment, where users tested the system properties or components based on their perspectives, via quantitative or qualitative methods [27]. Health-related measures were considered, alongside any health outcomes present in the included studies, such as diagnostic accuracy or symptom reduction.
Table 1. Overview and characteristics of included studies.
Table 2 has retained the characteristics of the CAs (the categories defined in Box 1) that were evaluated in the included studies. In addition, it shows AI methods used, based on a list of keywords, defined from three systematic literature reviews for CAs in health care [1,6,27].
Table 2. Characteristics of the conversational agents evaluated in the included studies.

3. Results

The six databases that were searched retrieved 1754 articles. Then, the duplicates were removed, which resulted in 1087 unique articles. After the abstract and title screening, 110 articles remained. After the full-text screening, 80 of these were excluded. Thirty articles were considered eligible for inclusion in the systematic literature review. Four more papers were excluded during extraction data based on the exclusion criteria. Twenty-six articles were considered eligible for inclusion in the systematic literature review (Figure 1).
Figure 1. Flow Diagram.

3.1. Description of Included Studies

The complete list of included studies (26 studies) used CAs to support tasks undertaken by patients (n = 14), clinicians (n = 1), and both (n = 11). Fourteen studies focused on patients mostly supporting education and self-care [28,29,30,31,32,33,34,35,36,37,38,39,40,41]. One study focused on clinicians, where the CAs were used to educate and increase awareness of the introductory psychology students in mental and physical health [42]. Eleven further studies supported patients and clinicians, where seven used CAs in treatment, education, and data collection [43,44,45,46,47,48,49]. Two studies helped in following-up [50,51], and two studies promoted medication adherence [52,53]. The most common chronic condition was diabetes (n = 5). Two studies were for type 1 [29,39], one study was for type 2 [28], and one focused on diabetes in general, whether the patients have type 1 or 2 [40]. One study focused on prediabetes with obesity [51]. The other four studies concentrated on different aspects of depression, such as symptoms and disorders [37,45,46,52]. Three studies dealt with cancer (breast cancer, geriatric oncology, after cancer treatment) [34,47,50]. Anxiety that could lead to depression [32,48], mental health [33,42], and schizophrenia [30,44] were the focus of two studies for each condition. Other conditions included hypertension [43], heart failure [38], HIV [53,54,55,56], long-term conditions [35], chronic pain [36], chronic problems in oral health [41], urinary incontinence [31], and post-traumatic stress disorder [49]. In terms of methods, the methods used in most studies were RCT, pilot study, or quasi-experimental. Only two studies had used mixed methods, and the remaining studies used other methods.

3.2. Description of Conversational Agents and AI Methods

Different technologies have supported CAs, including independent platforms, apps delivered via web or mobile device, short message services (SMS), and telephone (Table 2). Out of 26 conversational agents, 16 were chatbots (a computer program that simulates human conversation via voice or text communication). Seven were embodied conversational agents (ECA), a virtual agent that appeared on computer screens and was equipped with a virtual, human-like body that had real-time conversations with humans. One was a conversational agent in a robot, and another was a relational agent explicitly designed to remember history and manage future expectations in their interactions with users. One agent was not specified [43]. The characterisation of conversational agents are as shown in Table 3, and this summarization is adapted from Laranjo et al. 2018 [27].
Table 3. Characterisation of conversational agents (Laranjo et al. 2018 [27]).
The CAs in the papers used various AI methods such as speech recognition, facial recognition, and NLP. However, most studies did not provide sufficient information on the implementation details. In order to identify the AI methods, a list of common words (Appendix B) used for building AI CAs [1,6,27] were employed. Several papers reported that AI methods could improve the user’s interaction with the system [1,2,5,6,27]. For example, speech recognition can capture speech much faster than you can type. Half of the included papers utilized speech recognition in many CAs (e.g., chatbot, ECA, or relational agent). Although having speech recognition can capture speech much faster than typing, it could lead to difficulties with some keywords because of misinterpretation of words. Six studies did not report these technical methods.

3.3. Evaluation Measures

Evaluation measures were identified based on three types: technical performance (six studies), user experience (25 studies), and health-related measures (18 studies). The most common technical performance measures were accuracy (89–99.2% for five CAs) [31,37,40,43,48] and specificity (93–99.7% for three CAs) [37,40,46]. One study for hypertension identified that the rate of the achieved goal for the CAs was 96%. In addition, the authors clarified that the accuracy of the spoken dialogue system in cough and compliance were 81% and 41%, respectively [43]. Another study in glaucoma and diabetic conditions used Cohen’s D to calculate the task completion (k = 0.848), and the accuracy of the CAs was 89% [40]. Two studies were on depression (symptoms and major depressive disorder) and used finite-state dialogue management. The study for symptoms of depression noted that the written chatbot showed an accuracy of 99.2% and a specificity of 99.7% [52]. The spoken system for a major depressive disorder used embodied CAs, and showed sensitivity (49%) and specificity (93%) [46]. Two studies were about treating urinary incontinence and various chronic conditions such as pain and anxiety. The urinary incontinence article mentioned accuracy, but without clarifying the percentage or rate of accuracy [31]. Another paper for various chronic conditions (pain, anxiety, and depression) showed almost 92% accuracy in the breathing rate for patients [48].
Almost all studies reported on user experience except one study [43]. Helpfulness, satisfaction, and ease of use were the common features in more than half of the included studies. Three studies mentioned that users were unsatisfied. In two studies, the participants found the CAs hard to use. Regarding diabetes–type 2 [28], a study reported the feedback from patients through various measures, such as competency (85%), helpfulness and friendliness (86%). On the other hand, some patients described the embodied CAs as annoying (39%) and boring (30%). Another study for diabetes [40] illustrated the user experience through attractiveness (0.74), perspicuity (0.67), and efficiency (0.77), by using the scale of Cronbach’s Alpha Coefficient correlation. In mental health, a study for treatment and education reported that some users felt the chatbot was hard to engage with and had no availability to ask questions [33]. A study after cancer treatment clarified that the users found the chatbot nonjudgmental and helpful. Additionally, users supported recommending it to a friend (69%).
Regarding health-related measures, 18 out of the 26 studies included the health-related measures. The most common method that has been used is quasi-experimental, where it was used in 12 out of 26 studies. The second most common method used was RCT, used in six studies. One of the quasi-experimental studies evaluated the medication adherence system of interaction dialogue, finding decreased delirium (p < 0.001) and loneliness (p = 0.01) [45]. Another study showed a reduction in depression and anxiety by p = 0.053 and 0.029, respectively [32]. One RCT measured the outcomes using a 5-point Likert-type scale, finding improved self-management for older people with the chatbot (p = 0.001) [28]. One study reported quasi-experimental and RCT which evaluated a medication adherence intervention, finding that system use, medication adherence, physical activity, and satisfaction measures were high (84–89%) [44].

4. Discussion

The most commonly used method in the included studies was quasi-experimental, which was used in almost half of the included papers. This is aligned with the findings of the previous systematic reviews of CAs in healthcare [1,27]. Quasi-experimental demonstrates the involvement of real-world interventions, instead of artificial laboratory settings. It allows the research to move with higher internal validity than other non-experimental types of research. In addition, quasi-experimental design requires fewer resources and is less expensive compared with RCT. This systematic review introduced a list of AI CAs in healthcare for chronic disease. It reflects the efficiency, acceptability, and usability of the AI CAs in the daily education of, and support for, chronic disease patients. Our review reflected this as most of the included studies were published after 2016 (21 papers). Most included studies evaluated task-oriented AI CAs (23 studies out of 26) that are used to assist patients and clinicians through specific processes. The majority of the included studies were focused entirely on designing, developing, or evaluating AI CAs that are specific to one chronic condition. This finding implies that AI CAs evolve to provide tailored support for specific chronic conditions, rather than general interventions for a broad range of chronic conditions.
The outcomes of the included studies were assessed on three measures: technical performance, user experience, and a health-related measure. There were only six studies that mentioned some technical details. Due to the lack of details reported on the technical implementation of AI methods, it was not possible to establish consistent relationships with the intervention used, disease areas, and measured outcomes. The evaluation measures of the identified AI-based CAs and their effects on the targeted chronic conditions were not unified and broad. This inconsistency shows the complexity of contrasting and comparing the current AI CAs. Regardless of some studies that showed the complexity in use and chatbot constraints (four studies), most studies reported satisfaction with agents and feeling more comfortable than continuous follow-ups with a doctor in the hospitals. User experience was the most commonly reported measure (25 studies). It reflects the positive effect and enhancement of the quality of life in most studies through AI CAs that help patients who suffer from a chronic condition. This systematic review found that most included studies focused on designing, developing, or evaluating AI CAs for a specific chronic condition. That resulted in more accuracy, a tailored interaction with patients, and enhanced interventions for a wide range of conditions. In dialogue management, nine studies used a mixed initiative, whereas most applied system initiatives. No study in the included studies targeted the dialogue management of agent-based interactions. Moreover, these studies do not contain CAs that can be used across other populations. No analysis is applied on a broad scale, especially for communities or countries that suffer a lot from managing chronic conditions due to the high demand on hospitals or the cost and effort of following up with doctors. Targeting this area will help many people deal with chronic conditions and live their lives, especially as the CAs’ supporting preventive measures can prove very effective.
Compared to prior reviews focused on AI CAs for healthcare, we found only two review studies that targeted AI CAs for chronic conditions, where one of them focused on voice-based CAs only. Those reviews did not differentiate between the type of CAs used besides the AI methods used in each study, so this review focused on investigating the different types of dialogue management with the AI method used in each study. This review also focused on technical descriptions of the CAs used. Clarifying the technical features of the AI CAs will help to choose the appropriate type of AI CAs. Regarding limitations, most studies did not include technical performance details, which makes replicability of the studies reviewed problematic. Another limitation of the reviewed literature is the heterogeneity and the prevalence of quasi-experimental studies. This suggests that this is still a nascent field.

5. Conclusions

Many studies in this review showed some positive evidence for the usefulness and usability of AI CAs to support the management of different chronic diseases. The overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of the included studies. Although the users in many studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions. This is mainly due to the insufficient reporting of technical implementation details. Future research studies should provide more detailed accounts of the technical aspects of the CAs used. This includes developing a comprehensive and clear taxonomy for the CAs in healthcare. More RCT studies are required to evaluate the efficacy of using AI CAs to manage chronic conditions. Safety aspects of CAs is still a neglected area, and needs to be included as part of core design considerations.

Author Contributions

Conceptualization, A.B.S., M.P. and A.B.K.; methodology, A.B.S., B.N., M.P., D.P. and A.B.K.; software, A.B.S., A.A., A.M., I.M., J.S. and B.Y.; validation, A.B.S., A.A., A.M., I.M., J.S. and B.Y.; formal analysis A.B.S., A.A., A.M., I.M., J.S. and B.Y., investigation, A.B.S., B.N., D.P., M.P. and A.B.K.; resources, A.B.S., M.P. and A.B.K.; data curation, A.B.S., A.A. and I.M.; writing—original draft preparation, A.B.S.; writing—review and editing, A.B.S., B.N., A.A., A.M., I.M., J.S., B.Y., D.P., M.P. and A.B.K.; visualization, I.M., J.S. and B.Y.; supervision, B.N., M.P. and A.B.K.; project administration, A.A., A.M. and I.M.; funding acquisition, B.N., M.P., D.P. and A.B.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Study Protocol

Adopted from PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) and PROSPERO.
TopicContent
TitleConversational Healthcare Agent for Chronic Diseases: A Systematic Review
AuthorsAbdullah Bin Sawad, Baki Kocaballi, Mukesh Prasad, Bhuva Narayan, Ahlam Alnefaie, Ashwaq Maqbool, Indra Mckie, Jemma Smith, Berkan Yuksel
Review team members and their organisational affiliationsAbdullah Bin Sawad 1(PhD Student)
Dr Baki Kocaballi 1(Lecturer)
Dr Mukesh Prasad 1(Senior Lecturer)
Dr Bhuva Narayan 2(Associate Professor)
Ahlam Alnefaie 1(PhD Student)
Dr Ashwaq Maqbool 3(Master Student)
Indra Mckie 1(PhD Student)
Jemma Smith 4(Bachelor Student)
Berkan Yuksel 1
Deepak Puthal 5		(Bachelor Student)
(Assistant Professor)
Contact details of the corresponding authorAbdullah Bin Sawad
abdullahhatima.binsawad-1@student.uts.edu.au
Organisational affiliation of the reviewUniversity of Technology Sydney
Type and method of reviewSystematic literature review
ContributionsStudy design: AS; Search strategy: AS; Screening: AS, AA, IM Data extraction and Data analysis: AS, AM, JS, BY; First draft: AS; Revisions and subsequent drafts: BK, MP, BN, DP; Critical feedback for the final draft: BK, MP, BN, DP
Sources/SponsorsNA
Conflict of interestNone
RationaleWhat kinds of conversational agents are used for chronic conditions, what type of communication technology, what AI methods are used, what are the outcomes, the research gaps, and who are the target users/population group.
Eligibility criteriaInclusion Criteria
  • We will include primary research studies that (i) focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases; (ii) involved conversational agent and AI methods used; and (iii) tested the system with human users.

Exclusion Criteria
  • Review, perspective, opinion papers, or news articles will be excluded.
  • Studies must also have reported evaluations based on human users interacting with the full system. Studies evaluating only individual components of the conversational agent—automatic speech recognition, natural language understanding, dialogue management, response generation, text-to-speech synthesis –will be excluded.
  • Studies will be excluded using “Wizard of Oz” methods, where the dialog is generated by a human operator rather than the conversational agent.
Information sourcesA database search will be conducted by accessing PubMed Medline, EMBASE, PsycINFO, CINAHL, ACM Digital Library, and Web of Science databases. Search terms include synonyms, acronyms, and commonly known terms of the constructs “conversational agent” and “healthcare”. Grey literature will be excluded, such as posters, reviews, and presentations.
Search strategyThe following search strategy will be used in the whole six databases.
Filters: none
Conduct started in February 2021
“Conversational agent” OR “conversational agents” OR “conversational system” OR “conversational systems” OR “dialog system” OR “dialog systems” OR “dialogue systems” OR “dialogue system” OR “assistance technology” OR “assistance technologies” OR “relational agent” OR “relational agents” OR “chatbot” OR “chatbots” OR “digital agent” OR “digital agents” OR “digital assistant” OR “digital assistants” OR “virtual assistant” OR “virtual assistants” AND “healthcare” OR “digital healthcare” OR “digital health” OR “health” OR “mobile health” OR “mHealth” OR “mobile healthcare”.
Type of included studyAny primary research
Studied domainChronic health conditions
Population/ParticipantsAny population and any participants (caregivers, healthcare professionals, clinical/non-clinical, patients)
Data collection and selection processAS and AA will conduct the initial screening of the obtained studies based on titles and abstracts. Then, AS and IM will conduct full-text screening based on the eligibility/inclusion criteria. AS, AM, JS, and BY will extract data from eligible papers. Any disagreement will be discussed in the zoom meeting. Dr. Kocaballi and Dr. Prasad will supervise all these processes to ensure the measures are on the right path.
Data items for codingThe following data items will be extracted from each included study: first author, year of publication, study location, study design/type, study aim, conversational agent evaluation measures, main reported outcomes and findings, type of chronic condition, type of study participants, type of the conversational agent, the goal of the conversational agent, communication channel, interaction modality, technique, system development. AS, AM, JS and BY will conduct the data extraction, and it will be discussed with Kocaballi and Dr Prasad.
Outcomes and prioritisationMain outcomes: Any healthcare related intervention outcomes (e.g., type of chronic condition, health goal, intervention targets), any architecture related outcomes (e.g., technique type, system development).
Additional outcomes: Any conversational agent related outcomes (e.g., feasibility, accuracy, acceptability, functionality) and design features.
Risk of bias in individual studiesAS and IM will review the included papers to appraise their quality. Disagreement will be discussed to reach a consensus. Any disagreement will be resolved with Dr Kocaballi and Dr. Prasad.
Data synthesisThe PRISMA guidelines will be used for data synthesis. A narrative synthesis of the included studies will be performed.
LanguageEnglish
CountryAustralia
Anticipated or actual start dateFebruary 2021
Anticipated or actual end dateSeptember 2021
1 School of Computer Science, Faculty of Engineering and IT, University of Technology Sydney. 2 School of Communication, Faculty of Arts and Social Sciences, University of Technology Sydney. 3 School of Public Health, Faculty of Medicine and Health, The University of Sydney. 4 School of Biomedical Engineering, Faculty of Engineering and IT, University of Technology Sydney. 5 Department of Electrical Engineering and Computer Science, Khalifa University.

Appendix B

Keywords for AI Methods Used
Artificial Intelligence or AI
Natural Language Understanding or NLU
Natural Language Processing or NLP
NR
neural networks
deep learning
machine learning
clustering/classification
unsupervised/supervised learning
CNN or convolutional neural network
Markov chain
hidden Markov chain
reinforcement learning
facial recognition
speech recognition
text analysis
sentiment analysis
natural language generation
text-to-speech or TTS
speech-to-text or STT
synthetic speech
spoken dialog system

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