Conversational agents in healthcare: a systematic review

By Liliana Laranjo, A. Dunn, H. L. Tong, A. Kocaballi, Jessica A. Chen, R. Bashir, Didi Surian, B. Gallego, F. Magrabi, Annie Y. S. Lau and 1 more. J. Am. Medical Informatics Assoc., 2018

1,152 citations recorded by the source corpus.

Abstract 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.

Subfields: Applied Psychology, Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/1abcc5349eeec3608f87caa32fcdb9baa24a3265

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Psychology

In Artificial Intelligence

In Health Informatics

Rifat A. Atun

Citations in the corpus

Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020, background): “A recent systematic review on the effectiveness of ECAs and other conversational agents noted a lack of an established method for evaluating health care conversational agents in health care and a dearth of data on adverse effects [32].”
Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review (2023, positive): “Artificial intelligence (AI)–driven chatbots (AI chatbots) are conversational agents that mimic human interaction through written, oral, and visual forms of communication with a user [1,2].”
AI in mental health. (2020, background): “The underlying system can be based on a variety of foundations, ranging from a set of simple rule-based responses and keyword matching to sophisticated NLP techniques [35,36].”
ConveRT: Efficient and Accurate Conversational Representations from Transformers (2019, background): “…ﬂights (Hemphill et al., 1990; Williams, 2012; El Asri et al., 2017), with further use in tourist information (Budzianowski et al., 2018), language learning (Raux et al., 2003; Chen et al., 2017), entertainment (Fraser et al., 2018), and healthcare (Laranjo et al., 2018; Fadhil and Schiavo, 2019).”
Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners (2023, background): “Additionally, conversational agents in healthcare have shown promise in delivering tailored education and facilitating patient engagement (39).”
The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality (2021, background): “However, their evidence base is not well established 85 .”
Artificial intelligence in healthcare: An essential guide for health leaders (2019, background): “However, the reliability of such text-based chatbots is often achieved at the cost of restricting user input to predetermined words and phrases, not allowing users to take the initiative in the dialogue.(6) With the high expectation of investors and the efforts from both big tech companies and start”
A Wizard-of-Oz Interface and Persona-based Methodology for Collecting Health Counseling Dialog (2020, background): “Their efficacy in health counseling dialog has been demonstrated in a number of randomized controlled trials [24,26,30].”
An overview of the features of chatbots in mental health: A scoping review (2019, methodology): “In addition that such guidelines improve reporting of studies, they enable readers to assess the quality of studies, combine their results, and compare between interventions [29].”
Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review (2021, neutral): “With their ability for complex dialog management and conversational flexibility, integration of chatbot technology into clinical practice may reduce costs, refine workflow efficiencies, and improve patient outcomes [5].”
The Personalization of Conversational Agents in Health Care: Systematic Review (2019, background): “A recent systematic review in this area examined technical performance, user experience, and health-related outcomes and found that most studies had not employed standardized evaluation methods or had failed to address aspects of patient safety [2].”