Evidence map›Paper›PMID 42311954›Full record

ArticleFrontiers in medical technology2026

Public trust in AI-enabled telemedicine: affective, cognitive, and structural dimensions insights from multi-platform big data analytics.

Faisal Binsar, Mohammad Hamsal, Erwin Tenggono, Maman Abdurohman, Ridha Hanafi, Indra Wahyudi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in medical technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Faisal BinsarManagement Department, Binus Online Learning, Bina Nusantara University, Jakarta, Indonesia.
Mohammad HamsalManagement Department, BINUS Business School Doctor of Research in Management, Bina Nusantara University, Jakarta, Indonesia.
Erwin TenggonoFaculty of Economics and Business, Universitas Pelita Harapan, Tangerang, Indonesia.
Maman AbdurohmanSchool of Computing, Telkom University, Bandung, Indonesia.
Ridha HanafiInformation System Department, Faculty of Industrial Engineering, Telkom University, Bandung, Indonesia.
Indra WahyudiFaculty of Naval Architecture, Politeknik Negeri Bengkalis, Bengkalis, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is transforming healthcare delivery, with telemedicine emerging as one of its most viable and socially impactful applications, raising important questions about how public trust is formed and sustained in AI-enabled healthcare environments. Yet, despite rapid technological adoption, public trust in AI-enabled telemedicine remains insufficiently understood, particularly across the diverse online environments where perceptions are collectively shaped. This study employs a sociotechnical, multi-platform big data analytics approach to quantify and interpret public trust in large-scale digital discourse. A total of 25,396 online mentions collected between 14 September and 14 October 2025 from news, social media, blogs, video, and web platforms were analyzed using a hybrid framework combining lexicon-based sentiment analysis (

Indexed as

AI-enabled telemedicinebig data analyticsdigital healthhealth informaticspublic trustsentiment analysis

Identifiers

PMID42311954
PMCPMC13269267

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.