Conversational agents in healthcare: a systematic review.

Objective: Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes. Methods: We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen's kappa measured inter-coder agreement. Results: The database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies. Conclusions: The use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting. Protocol Registration: The protocol for this systematic review is registered at PROSPERO with the number CRD42017065917

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

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

Conversational Agents for depression screening: a systematic review

Objective: This work explores the advances in conversational agents aimed at the detection of mental health disorders, and specifically the screening of depression. The focus is put on those based on voice interaction, but other approaches are also tackled, such as text-based interaction or embodied avatars. Methods: PRISMA was selected as the systematic methodology for the analysis of existing literature, which was retrieved from Scopus, PubMed, IEEE Xplore, APA PsycINFO, Cochrane, and Web of Science. Relevant research addresses the detection of depression using conversational agents, and the selection criteria utilized include their effectiveness, usability, personalization, and psychometric properties. Results: Of the 993 references initially retrieved, 36 were finally included in our work. The analysis of these studies allowed us to identify 30 conversational agents that claim to detect depression, specifically or in combination with other disorders such as anxiety or stress disorders. As a general approach, screening was implemented in the conversational agents taking as a reference standardized or psychometrically validated clinical tests, which were also utilized as a golden standard for their validation. The implementation of questionnaires such as Patient Health Questionnaire or the Beck Depression Inventory, which are used in 65% of the articles analyzed, stand out. Conclusions: The usefulness of intelligent conversational agents allows screening to be administered to different types of profiles, such as patients (33% of relevant proposals) and caregivers (11%), although in many cases a target profile is not clearly of (66% of solutions analyzed). This study found 30 standalone conversational agents, but some proposals were explored that combine several approaches for a more enriching data acquisition. The interaction implemented in most relevant conversational agents is textbased, although the evolution is clearly towards voice integration, which in turns enhances their psychometric characteristics, as voice interaction is perceived as more natural and less invasive.Agencia Estatal de Investigación | Ref. PID2020-115137RB-I0

Interactive online health promotion interventions:a “health check”

As an increasingly popular medium by which to access health promotion information, the Internet offers significant potential to promote (often individualized) health-related behavioral change across broad populations. Interactive online health promotion interventions are a key means, therefore, by which to empower individuals to make important well being and treatment decisions. But how ldquohealthyrdquo are interactive online health promotion interventions? This paper discusses a literature review (or ldquohealth checkrdquo) of interactive online health interventions. It highlights the types of interactive interventions currently available and identifies areas in which research attention is needed in order to take full advantage for the Internet for effective health promotion

Studies on Inequalities in Information Society. Proceedings of the Conference, Well-Being in the Information Society

Siirretty Doriast

Speech Recognition of Aged Voices in the AAL Context: Detection of Distress Sentences

International audienceBy 2050, about a third of the French population will be over 65. In the context of technologies development aiming at helping aged people to live independently at home, the CIRDO project aims at implementing an ASR system into a social inclusion product designed for elderly people in order to detect distress situations. Speech recognition systems present higher word error rate when speech is uttered by elderly speakers compared to when non-aged voice is considered. Two specialized corpora in French, AD80 and ERES38, were recorded in this framework by aged people, they were used first to study the possibility of adaptation of standard ASR to aged voice. Then we looked at whether the variability of the WER between speakers could be correlated with the level of dependence. Then, we assessed the performance of distress sentence detection by a filter and we demonstrated a significant drop in performance for those with the lowest degree of autonomy

Robust Dialog Management Through A Context-centric Architecture

This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user’s goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine’s ability to communicate may be hindered by poor reception of utterances, caused by a user’s inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by ContextBased Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user’s assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users