Human-Computer Speech is gaining momentum as a technique of computer interaction. There has been a recent upsurge in speech based search engines and assistants such as Siri, Google Chrome and Cortana. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to analyse speech, and intelligent responses can be found by designing an engine to provide appropriate human like responses. This type of programme is called a Chatbot, which is the focus of this study. This paper presents a survey on the techniques used to design Chatbots and a comparison is made between different design techniques from nine carefully selected papers according to the main methods adopted. These papers are representative of the significant improvements in Chatbots in the last decade. The paper discusses the similarities and differences in the techniques and examines in particular the Loebner prize-winning Chatbots.

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Survey on Chatbot Design Techniques in Speech Conversation Systems

Background: Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care.
Objective: This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application.
Methods: We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms “conversational agents,” “conversational AI,” “chatbots,” and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis.
Results: The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education.
Conclusions: The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence–driven, and smartphone app–delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.

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Conversational Agents in Health Care: Scoping Review and Conceptual Analysis.

Artificial intelligence (AI) has transformed the world and the relationships among humans as the learning capabilities of machines have allowed for a new means of communication between humans and machines. In the field of health, there is much interest in new technologies that help to improve and automate services in hospitals. This article aims to explore the literature related to conversational agents applied to health care, searching for definitions, patterns, methods, architectures, and data types. Furthermore, this work identifies an agent application taxonomy, current challenges, and research gaps. In this work, we use a systematic literature review approach. We guide and refine this study and the research questions by applying Population, Intervention, Comparison, Outcome, and Context (PICOC) criteria. The present study investigated approximately 4145 articles involving conversational agents in health published over the last ten years. In this context, we finally selected 40 articles based on their approaches and objectives as related to our main subject. As a result, we developed a taxonomy, identified the main challenges in the field, and defined the main types of dialog and contexts related to conversational agents in health. These results contributed to discussions regarding conversational health agents, and highlighted some research gaps for future study.

Survey of conversational agents in health

Opinion Paper: "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Introduction: Benefits from mental health early interventions may not be sustained over time, and longer-term intervention programs may be required to maintain early clinical gains However, due to the high intensity of face-to-face early intervention treatments, this may not be feasible Adjunctive internet-based interventions specifically designed for youth may provide a cost-effective and engaging alternative to prevent loss of intervention benefits However, until now online interventions have relied on human moderators to deliver therapeutic content More sophisticated models responsive to user data are critical to inform tailored online therapy Thus, integration of user experience with a sophisticated and cutting-edge technology to deliver content is necessary to redefine online interventions in youth mental health This paper discusses the development of the moderated online social therapy (MOST) web application, which provides an interactive social media-based platform for recovery in mental health We provide an overview of the system's main features and discus our current work regarding the incorporation of advanced computational and artificial intelligence methods to enhance user engagement and improve the discovery and delivery of therapy content Methods: Our case study is the ongoing Horyzons site (5-year randomized controlled trial for youth recovering from early psychosis), which is powered by MOST We outline the motivation underlying the project and the web application's foundational features and interface We discuss system innovations, including the incorporation of pertinent usage patterns as well as identifying certain limitations of the system This leads to our current motivations and focus on using computational and artificial intelligence methods to enhance user engagement, and to further improve the system with novel mechanisms for the delivery of therapy content to users In particular, we cover our usage of natural language analysis and chatbot technologies as strategies to tailor interventions and scale up the system Conclusions: To date, the innovative MOST system has demonstrated viability in a series of clinical research trials Given the data-driven opportunities afforded by the software system, observed usage patterns, and the aim to deploy it on a greater scale, an important next step in its evolution is the incorporation of advanced and automated content delivery mechanisms

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Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health.

In wireless mesh network environment, available bandwidth (ABW) estimation is important to provide feedback on the quality of network infrastructure to end user. Recent study shows the emerging of Round Trip (RT) based ABW tools where it eliminates the need of having a receiver end. However, these tools are not fully tested in various network environment and benchmark with existing widely used ABW tools. In this paper, we perform an extensive experimental analysis study on RT based tools with other widely used ABW tools by looking at the accuracy and consistency. In term of accuracy and consistency, RT based tools excel in these conditions.

Abbas Saliimi Lokman

Papers

Performance analysis on round trip available bandwidth estimation tools for wireless mesh network