Evidence map›Paper›PMID 42422856›Full record

ArticleFrontiers in medicine2026

Trust and verification in AI-enabled physician chatbots for chronic disease management: evidence from digital health behavior.

Haitham Alzghaibi

Abstract read
In one paragraph

Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Haitham AlzghaibiDepartment of Health Informatics, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Advances in digital health technologies have transformed how individuals with chronic diseases seek and use health information. Patients increasingly rely on online sources, including search engines, social media, and messaging applications, to understand symptoms and manage chronic conditions. However, these digital environments can also expose users to misinformation or conflicting advice. Artificial intelligence (AI) enabled tools and mobile health (mHealth) services have emerged to assist patients in identifying symptoms, verifying health information, and supporting timely health decisions. Despite these developments, limited conceptual work has examined how individuals with chronic diseases integrate such tools into their health information-seeking and decision-making processes. Objective: This study aimed to develop and empirically illustrate a model explaining how individuals with chronic diseases seek and verify digital health information using AI-enabled tools and how these processes influence trust and health-related decision-making. Methods: A cross-sectional survey was conducted among adults aged ≥ 18 years diagnosed with diabetes or hypertension. Participants were recruited through chronic disease support groups on Facebook and WhatsApp. The survey assessed digital health information-seeking behavior, verification practices, trust in AI-enabled physician chatbots and national mHealth services, and their role in health-related decision-making. Descriptive statistics and visualization analyses were conducted using R. Results: Health information seeking occurred across multiple digital platforms, with considerable overlap between messaging applications, social media, and web-based sources. Participants reported using AI physician chatbots and national mHealth services mainly to verify health information encountered online. Trust in AI diagnostic support tools was moderate, indicating cautious but active engagement. Most participants used these tools to support clinical consultations rather than replace professional medical advice. Conclusion: Verification behavior and trust play key roles in how individuals with chronic diseases engage with digital health information. AI-enabled mHealth tools may function as complementary decision-support resources that help patients verify information and interpret symptoms while supporting informed health decisions alongside traditional healthcare services.

Indexed as

AI physician chatbotschronic disease managementdigital health verificationhealth information seekingmHealth platforms

Identifiers

PMID42422856
PMCPMC13341845

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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.