Evidence map›Paper›PMID 35408238›Full record

SynthesisSensors (Basel, Switzerland)2022

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

Abdullah Bin Sawad, Bhuva Narayan, Ahlam Alnefaie, Ashwaq Maqbool, Indra Mckie, Jemma Smith, Berkan Yuksel, Deepak Puthal, Mukesh Prasad, A Baki Kocaballi

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Abdullah Bin SawadSchool of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, Australia.ORCID 0000-0002-0200-617X
Bhuva NarayanSchool of Communication, Faculty of Arts and Social Sciences, University of Technology Sydney, Sydney 2007, Australia.
Ahlam AlnefaieSchool of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, Australia.ORCID 0000-0002-1172-902X
Ashwaq MaqboolSchool of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney 2007, Australia.
Indra MckieSchool of Communication, Faculty of Arts and Social Sciences, University of Technology Sydney, Sydney 2007, Australia.
Jemma SmithSchool of Biomedical Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, Australia.
Berkan YukselSchool of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, Australia.
Deepak PuthalDepartment of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates.ORCID 0000-0002-8332-278X
Mukesh PrasadSchool of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, Australia.ORCID 0000-0002-7745-9667
A Baki KocaballiSchool of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceCommunicationChronic DiseaseDelivery of Health CareHumansInformation Technologychatbotconversational agentsdialogue systemsrelational agents

Identifiers

PMID35408238
PMCPMC9003264

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.