Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 11, 2025
Date Accepted: Mar 12, 2026
Patient Concerns Regarding Artificial Intelligence Applications in Healthcare: A Systematic Review and Meta-Synthesis Based on Social Ecological Theory
ABSTRACT
Background:
Artificial intelligence (AI) in healthcare demonstrates significant potential in diagnosis, treatment, and health management. However, rapid advancement has raised patient concerns regarding privacy, transparency, doctor-patient relationships, and healthcare equity. Theoretical frameworks examining these concerns remain underdeveloped.
Objective:
To systematically synthesize qualitative evidence on patient concerns regarding AI healthcare applications and analyze these through social ecological theory, revealing multi-level interaction mechanisms that may lead to "ecological imbalance" in AI-driven healthcare systems.
Methods:
A systematic review searched Embase, PubMed, and Web of Science for qualitative studies on patient concerns about AI in healthcare. Quality assessment used the JBI-QARI instrument; studies scoring ≤5 were excluded. Two researchers independently performed meta-synthesis, coding and synthesizing findings into integrated themes.
Results:
Results:
Eleven studies were included, yielding six core themes: (1) privacy and data security concerns; (2) technological limitations and reliability; (3) impact on doctor-patient relationships; (4) trust and accountability issues; (5) ethical challenges and healthcare equity; and (6) future outlook. Social ecological analysis revealed "ecological imbalance" risks across three levels: micro (technology cognition and data control anxiety), meso (fractured doctor-patient trust and institutional accountability deficits), and macro (healthcare inequity and lagging ethical standards). These concerns form mutually reinforcing cycles through risk perception transmission.
Conclusions:
Conclusions:
Patient concerns reflect complex interactions among individual cognition, interpersonal relationships, organizational governance, and social equity. Addressing these requires multi-level strategies: enhancing technological transparency at the micro level, strengthening doctor-patient communication and accountability mechanisms at the meso level, and advancing unified ethical standards and equitable policies at the macro level, balancing technological progress with humanistic care
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