Background: Conversational agents (also known as chatbots) have evolved in recent decades to become multimodal, multifunctional platforms with potential to automate a diverse range of health-related activities supporting the general public, patients, and physicians. Multiple studies have reported the development of these agents, and recent systematic reviews have described the scope of use of conversational agents in health care. However, there is scarce research on the effectiveness of these systems; thus, their viability and applicability are unclear.
Objective: The objective of this systematic review is to assess the effectiveness of conversational agents in health care and to identify limitations, adverse events, and areas for future investigation of these agents.
Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols will be used to structure this protocol. The focus of the systematic review is guided by a population, intervention, comparator, and outcome framework. A systematic search of the PubMed (Medline), EMBASE, CINAHL, and Web of Science databases will be conducted. Two authors will independently screen the titles and abstracts of the identified references and select studies according to the eligibility criteria. Any discrepancies will then be discussed and resolved. Two reviewers will independently extract and validate data from the included studies into a standardized form and conduct quality appraisal.
Results: As of January 2020, we have begun a preliminary literature search and piloting of the study selection process.
Conclusions: This systematic review aims to clarify the effectiveness, limitations, and future applications of conversational agents in health care. Our findings may be useful to inform the future development of conversational agents and promote the personalization of patient care.
International Registered Report Identifier (irrid): PRR1-10.2196/16934.
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http://dx.doi.org/10.2196/16934 | DOI Listing |
Int J Nurs Stud
December 2024
School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China.; Research Centre for Chinese Medicine Innovation, The Hong Kong Polytechnic University, Hong Kong SAR, China.; Joint Research Centre for Primary Health Care, The Hong Kong Polytechnic University, Hong Kong SAR, China.. Electronic address:
Background: Effective management of physical and psychological symptoms is a critical component of comprehensive care for both chronic disease patients and apparently healthy individuals experiencing episodic symptoms. Conversational agents, which are dialog systems capable of understanding and generating human language, have emerged as a potential tool to enhance symptom management through interactive support.
Objective: To examine the characteristics and effectiveness of conversational agent-delivered interventions reported in randomized controlled trials (RCTs) in the management of both physical and psychological symptoms.
Adv Skin Wound Care
January 2025
At the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, United States, Adrian Chen, BS, Aleksandra Qilleri, BS, and Timothy Foster, BS, are Medical Students. Amit S. Rao, MD, is Project Manager, Department of Surgery, Wound Care Division, Northwell Wound Healing Center and Hyperbarics, Northwell Health, Hempstead. Sandeep Gopalakrishnan, PhD, MAPWCA, is Associate Professor and Director, Wound Healing and Tissue Repair Analytics Laboratory, School of Nursing, College of Health Professions, University of Wisconsin-Milwaukee. Jeffrey Niezgoda, MD, MAPWCA, is Founder and President Emeritus, AZH Wound Care and Hyperbaric Oxygen Therapy Center, Milwaukee, and President and Chief Medical Officer, WebCME, Greendale, Wisconsin. Alisha Oropallo, MD, is Professor of Surgery, Donald and Barbara Zucker School of Medicine and The Feinstein Institutes for Medical Research, Manhasset New York; Director, Comprehensive Wound Healing Center, Northwell Health; and Program Director, Wound and Burn Fellowship program, Northwell Health.
Generative artificial intelligence (AI) models are a new technological development with vast research use cases among medical subspecialties. These powerful large language models offer a wide range of possibilities in wound care, from personalized patient support to optimized treatment plans and improved scientific writing. They can also assist in efficiently navigating the literature and selecting and summarizing articles, enabling researchers to focus on impactful studies relevant to wound care management and enhancing response quality through prompt-learning iterations.
View Article and Find Full Text PDFSci Rep
January 2025
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Wundtlaan 1, 6525 XD Nijmegen, Nijmegen, The Netherlands.
Increasing evidence suggests that interlocutors use visual communicative signals to form predictions about unfolding utterances, but there is little data on the predictive potential of facial signals in conversation. In an online experiment with virtual agents, we examine whether facial signals produced by an addressee may allow speakers to anticipate the response to a question before it is given. Participants (n = 80) viewed videos of short conversation fragments between two virtual humans.
View Article and Find Full Text PDFFront Digit Health
December 2024
MOH Office for Healthcare Transformation, Singapore, Singapore.
The COVID-19 pandemic in Singapore led to limited access to mental health services, resulting in increased distress among the population. This study explores the potential benefits of offering a digital mental health intervention (DMHI), Wysa, as a brief and longitudinal intervention as part of the mindline.sg initiative launched by the MOH Office for Healthcare Transformation in Singapore.
View Article and Find Full Text PDFJMIR Aging
January 2025
Department of Computing, Faculty of Computer and Mathematical Sciences, Hong Kong Polytechnic University, Hung Hom, China (Hong Kong).
Background: Providing ongoing support to the increasing number of caregivers as their needs change in the long-term course of dementia is a severe challenge to any health care system. Conversational artificial intelligence (AI) operating 24/7 may help to tackle this problem.
Objective: This study describes the development of a generative AI chatbot-the PDC30 Chatbot-and evaluates its acceptability in a mixed methods study.
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