Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.

ChatGPT: Is this version good for healthcare and research?

Abstract: There have been advancements in artificial intelligence (AI) and deep learning in the past decade. Recently, OpenAI Inc. has launched a new chatbot, called ChatGPT that interacts in a conversational way and its dialogue format makes is user friendly and fast. In this paper we aimed to explore the current position and the accuracy of currently available version of ChatGPT in relation to healthcare and medical research.We searched the PubMed, Scopus, and Google databases from 15th to 25th February 2023, using the keywords: 'ChatGPT' AND 'medical research, healthcare, and scientific writing'. We found 29 results in PubMed and 9 results in Scopus database., in English language. In addition, we (RV, AM) interacted with ChatGPT multiple times to review accuracy of responses of various medical questions.Using literature search and interactions with ChatGPT with medical questions, we infer that this version generates answers rapidly but narrates data from existing internet literature in a general manner. However, as emphasised by the company in the landing page of ChatGPT, we found errors in responses to medical questions, Further, narrated data were limited up to September 2021. Positive features include admission of its limitations in medical field, and as it has been designed, learning from previous answers.Current version of ChatGPT may be useful in a limited manner as a narrative AI chatbot for medical personnel, however, researchers are advised to fact check all statements provided, keeping in mind its limitations.

Combined menu-based and natural-language-based communication with chatbots

Abstract: A user is allowed to communicate with a chatbot. A menu is provided to the user that includes a list of actions that can be performed by the user. Whenever natural language input asking a question is received from the user, this input is forwarded to the chatbot, a response to this input is received from the chatbot, this response is provided to the user, and the menu is again provided to the user. Whenever natural language input is received from the user requesting an action that is not one of the actions in the menu, this input is forwarded to the chatbot, a response to this input is received from the chatbot, where this response includes another menu that includes a list of subsequent actions that are related to the requested action and can be performed by the user, and this other menu is provided to the user.

A quality analysis of facebook messenger's most popular chatbots

Abstract: This work introduces a set of quality attributes for chatbots. The selection is grounded on scholarly but also reputed blog references from 2016 and 2017. In addition, attributes should be amenable to be extracted (semi) automatically. On these premises, we consider four attributes: "support of a minimal set of common commands", "foresee language variations in both inputs and ouput", "human-assistance provision" and "timeliness". These attributes are worked out for the 100 most popular chatbots in Facebook Messager. The aim is to look for correlations between these attributes and chatbot popularity in terms of number of "likes". Results show that there is no significance correlation with any of the attributes. However, the experiment come up with two main insights. First, the lack of common communication paterns that would permit users to move their experiences and expectations from one chatbot to another. Second, the existence of many programming errors that reflect that bot programming is still a nascent area.

The Use of Artificial Intelligence–Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations

Abstract: Background Overweight and obesity have now reached a state of a pandemic despite the clinical and commercial programs available. Artificial intelligence (AI) chatbots have a strong potential in optimizing such programs for weight loss. Objective This study aimed to review AI chatbot use cases for weight loss and to identify the essential components for prolonging user engagement. Methods A scoping review was conducted using the 5-stage framework by Arksey and O’Malley. Articles were searched across nine electronic databases (ACM Digital Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science) until July 9, 2021. Gray literature, reference lists, and Google Scholar were also searched. Results A total of 23 studies with 2231 participants were included and evaluated in this review. Most studies (8/23, 35%) focused on using AI chatbots to promote both a healthy diet and exercise, 13% (3/23) of the studies used AI chatbots solely for lifestyle data collection and obesity risk assessment whereas only 4% (1/23) of the studies focused on promoting a combination of a healthy diet, exercise, and stress management. In total, 48% (11/23) of the studies used only text-based AI chatbots, 52% (12/23) operationalized AI chatbots through smartphones, and 39% (9/23) integrated data collected through fitness wearables or Internet of Things appliances. The core functions of AI chatbots were to provide personalized recommendations (20/23, 87%), motivational messages (18/23, 78%), gamification (6/23, 26%), and emotional support (6/23, 26%). Study participants who experienced speech- and augmented reality–based chatbot interactions in addition to text-based chatbot interactions reported higher user engagement because of the convenience of hands-free interactions. Enabling conversations through multiple platforms (eg, SMS text messaging, Slack, Telegram, Signal, WhatsApp, or Facebook Messenger) and devices (eg, laptops, Google Home, and Amazon Alexa) was reported to increase user engagement. The human semblance of chatbots through verbal and nonverbal cues improved user engagement through interactivity and empathy. Other techniques used in text-based chatbots included personally and culturally appropriate colloquial tones and content; emojis that emulate human emotional expressions; positively framed words; citations of credible information sources; personification; validation; and the provision of real-time, fast, and reliable recommendations. Prevailing issues included privacy; accountability; user burden; and interoperability with other databases, third-party applications, social media platforms, devices, and appliances. Conclusions AI chatbots should be designed to be human-like, personalized, contextualized, immersive, and enjoyable to enhance user experience, engagement, behavior change, and weight loss. These require the integration of health metrics (eg, based on self-reports and wearable trackers), personality and preferences (eg, based on goal achievements), circumstantial behaviors (eg, trigger-based overconsumption), and emotional states (eg, chatbot conversations and wearable stress detectors) to deliver personalized and effective recommendations for weight loss.

A chatbot system as a tool to animate a corpus

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