TY - JOUR
T1 - Unlocking human-like conversations
T2 - Scoping review of automation techniques for personalized healthcare interventions using conversational agents
AU - Martins, Ana
AU - Londral, Ana
AU - L. Nunes, Isabel
AU - V. Lapão, Luís
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/FCT_DSAIPA_2020/DSAIPA%2FAI%2F0094%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00667%2F2020/PT#
This work was carried out under the project “CardioFollow.AI: An intelligent system to enhance patients' safety and remote surveillance in follow-up for cardiothoracic surgery,” funded by the National Foundation of Science and Technology under the reference DSAIPA/AI/0094/2020. We also acknowledge the financial support of the National Foundation of Science and Technology (FCT—MCTES) through the project UIDB/00667/2020 (UNIDEMI).
Publisher Copyright:
© 2024 The Author(s)
PY - 2024/5
Y1 - 2024/5
N2 - Background: Conversational agents (CAs) offer a sustainable approach to deliver personalized interventions and improve health outcomes. Objectives: To review how human-like communication and automation techniques of CAs in personalized healthcare interventions have been implemented. It is intended for designers and developers, computational scientists, behavior scientists, and biomedical engineers who aim at developing CAs for healthcare interventions. Methodology: A scoping review was conducted in accordance with PRISMA Extension for Scoping Review. A search was performed in May 2023 in Web of Science, Pubmed, Scopus and IEEE databases. Search results were extracted, duplicates removed, and the remaining results were screened. Studies that contained personalized and automated CAs within the healthcare domain were included. Information regarding study characterization, and human-like communication and automation techniques was extracted from articles that met the eligibility criteria. Results: Twenty-three studies were selected. These articles described the development of CAs designed for patients to either self-manage their diseases (such as diabetes, mental health issues, cancer, asthma, COVID-19, and other chronic conditions) or to enhance healthy habits. The human-like communication characteristics studied encompassed aspects like system flexibility, personalization, and affective characteristics. Seven studies used rule-based models, eleven applied retrieval-based techniques for content delivery, five used AI models, and six integrated affective computing. Conclusions: The increasing interest in employing CAs for personalized healthcare interventions is noteworthy. The adaptability of dialogue structures and personalization features is still limited. Unlocking human-like conversations may encompass the use of affective computing and generative AI to help improve user engagement. Future research should focus on the integration of holistic methods to describe the end-user, and the safe use of generative models.
AB - Background: Conversational agents (CAs) offer a sustainable approach to deliver personalized interventions and improve health outcomes. Objectives: To review how human-like communication and automation techniques of CAs in personalized healthcare interventions have been implemented. It is intended for designers and developers, computational scientists, behavior scientists, and biomedical engineers who aim at developing CAs for healthcare interventions. Methodology: A scoping review was conducted in accordance with PRISMA Extension for Scoping Review. A search was performed in May 2023 in Web of Science, Pubmed, Scopus and IEEE databases. Search results were extracted, duplicates removed, and the remaining results were screened. Studies that contained personalized and automated CAs within the healthcare domain were included. Information regarding study characterization, and human-like communication and automation techniques was extracted from articles that met the eligibility criteria. Results: Twenty-three studies were selected. These articles described the development of CAs designed for patients to either self-manage their diseases (such as diabetes, mental health issues, cancer, asthma, COVID-19, and other chronic conditions) or to enhance healthy habits. The human-like communication characteristics studied encompassed aspects like system flexibility, personalization, and affective characteristics. Seven studies used rule-based models, eleven applied retrieval-based techniques for content delivery, five used AI models, and six integrated affective computing. Conclusions: The increasing interest in employing CAs for personalized healthcare interventions is noteworthy. The adaptability of dialogue structures and personalization features is still limited. Unlocking human-like conversations may encompass the use of affective computing and generative AI to help improve user engagement. Future research should focus on the integration of holistic methods to describe the end-user, and the safe use of generative models.
KW - Artificial Intelligence
KW - Automation
KW - Conversational Agents
KW - Healthcare
KW - Natural Language Processing
KW - Personalization
UR - http://www.scopus.com/inward/record.url?scp=85186630678&partnerID=8YFLogxK
U2 - 10.1016/j.ijmedinf.2024.105385
DO - 10.1016/j.ijmedinf.2024.105385
M3 - Review article
C2 - 38428201
AN - SCOPUS:85186630678
SN - 1386-5056
VL - 185
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
M1 - 105385
ER -