Self-Disclosure and AI Conversational Agents in Healthcare Contexts: A Systematic Review and Meta-Analysis

Yajing LU; Xiyuan ZHOU; Guangchao C. FENG

Self-Disclosure and AI Conversational Agents in Healthcare Contexts: A Systematic Review and Meta-Analysis

Yajing LU^*
, Xiyuan ZHOU
, Guangchao C. FENG

^*Corresponding author for this work

Research output: Contribution to conference › Conference paper › peer-review

Abstract

The rising digital technology of artificial intelligence (AI) and large language models (LLM) has opened new opportunities for personalized support in healthcare contexts. This study systematically retrieved 2,197 citations through database searching and synthesized findings from 21 empirical quantitative studies using both systematic review and meta- analytic techniques. Relying on theoretical foundations, including the Computers Are Social Actors (CASA) paradigm, social presence theory, social penetration theory, and social exchange theory, this article proposes an integrated theoretical model. Through meta- analysis, the study quantitatively summarizes the existing knowledge on self-disclosure and critically identifies the major factors that contribute to self-disclosure in human-AI interactions in healthcare contexts, as well as the potential AI-related and health-related psychological outcomes. Contributions and limitations are discussed.

Original language	English
Publication status	Published - Jun 2025
Event	75th Annual International Communication Association Conference - Hyatt Regency Denver, Denver, United States Duration: 12 Jun 2025 → 16 Jun 2025 https://www.icahdq.org/mpage/ICA25 (Conference website) https://cdn.ymaws.com/www.icahdq.org/resource/resmgr/conference/2025/ICA25_Abstracts_Program.pdf (Conference program)

Conference

Conference	75th Annual International Communication Association Conference
Abbreviated title	ICA 2025
Country/Territory	United States
City	Denver
Period	12/06/25 → 16/06/25
Internet address	https://www.icahdq.org/mpage/ICA25 (Conference website) https://cdn.ymaws.com/www.icahdq.org/resource/resmgr/conference/2025/ICA25_Abstracts_Program.pdf (Conference program)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Cite this

@conference{3332511ceb264c1e917fd087c66dc7c2,

title = "Self-Disclosure and AI Conversational Agents in Healthcare Contexts: A Systematic Review and Meta-Analysis",

abstract = "The rising digital technology of artificial intelligence (AI) and large language models (LLM) has opened new opportunities for personalized support in healthcare contexts. This study systematically retrieved 2,197 citations through database searching and synthesized findings from 21 empirical quantitative studies using both systematic review and meta- analytic techniques. Relying on theoretical foundations, including the Computers Are Social Actors (CASA) paradigm, social presence theory, social penetration theory, and social exchange theory, this article proposes an integrated theoretical model. Through meta- analysis, the study quantitatively summarizes the existing knowledge on self-disclosure and critically identifies the major factors that contribute to self-disclosure in human-AI interactions in healthcare contexts, as well as the potential AI-related and health-related psychological outcomes. Contributions and limitations are discussed.",

author = "Yajing LU and Xiyuan ZHOU and FENG, \{Guangchao C.\}",

year = "2025",

month = jun,

language = "English",

note = "75th Annual International Communication Association Conference, ICA 2025 ; Conference date: 12-06-2025 Through 16-06-2025",

url = "https://www.icahdq.org/mpage/ICA25, https://cdn.ymaws.com/www.icahdq.org/resource/resmgr/conference/2025/ICA25\_Abstracts\_Program.pdf",

}

TY - CONF

T1 - Self-Disclosure and AI Conversational Agents in Healthcare Contexts

T2 - 75th Annual International Communication Association Conference

AU - LU, Yajing

AU - ZHOU, Xiyuan

AU - FENG, Guangchao C.

PY - 2025/6

Y1 - 2025/6

N2 - The rising digital technology of artificial intelligence (AI) and large language models (LLM) has opened new opportunities for personalized support in healthcare contexts. This study systematically retrieved 2,197 citations through database searching and synthesized findings from 21 empirical quantitative studies using both systematic review and meta- analytic techniques. Relying on theoretical foundations, including the Computers Are Social Actors (CASA) paradigm, social presence theory, social penetration theory, and social exchange theory, this article proposes an integrated theoretical model. Through meta- analysis, the study quantitatively summarizes the existing knowledge on self-disclosure and critically identifies the major factors that contribute to self-disclosure in human-AI interactions in healthcare contexts, as well as the potential AI-related and health-related psychological outcomes. Contributions and limitations are discussed.

AB - The rising digital technology of artificial intelligence (AI) and large language models (LLM) has opened new opportunities for personalized support in healthcare contexts. This study systematically retrieved 2,197 citations through database searching and synthesized findings from 21 empirical quantitative studies using both systematic review and meta- analytic techniques. Relying on theoretical foundations, including the Computers Are Social Actors (CASA) paradigm, social presence theory, social penetration theory, and social exchange theory, this article proposes an integrated theoretical model. Through meta- analysis, the study quantitatively summarizes the existing knowledge on self-disclosure and critically identifies the major factors that contribute to self-disclosure in human-AI interactions in healthcare contexts, as well as the potential AI-related and health-related psychological outcomes. Contributions and limitations are discussed.

M3 - Conference paper

Y2 - 12 June 2025 through 16 June 2025

ER -

Self-Disclosure and AI Conversational Agents in Healthcare Contexts: A Systematic Review and Meta-Analysis

Abstract

Conference

UN SDGs

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