Evidence map›Paper›PMID 42424345›Full record

ArticlePLOS digital health2026

Patient attitudes toward artificial intelligence in Jordanian Healthcare: A cross-sectional survey study.

Zeena Al-Dabbas, Laith Khandakji, Nour Al-Shatarat, Hala Alqaisiah, Yazan Ibrahim, Tala Awed, Harith Baik, Mohammad Dawoud, Raghad Al-Haj Ali, Zaid Telfah and 2 more

Abstract read
In one paragraph

Article in PLOS digital health, 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
–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

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

12 authors.

Zeena Al-DabbasFaculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan.
Laith KhandakjiFaculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan.
Nour Al-ShataratFaculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan.
Hala AlqaisiahFaculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
Yazan IbrahimFaculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
Tala AwedFaculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan.
Harith BaikFaculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan.
Mohammad DawoudFaculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan.
Raghad Al-Haj AliFaculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan.
Zaid TelfahFaculty of Medicine, Yarmouk University, Irbid, Jordan.
Yamamah Al-HmaidFaculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan.
Adham AlsharkawiDepartment of Mechatronics Engineering, School of Engineering, The University of Jordan, Amman, Jordan.ORCID https://orcid.org/0000-0002-9354-7341

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly integrated into healthcare delivery, yet patient acceptance in resource constrained settings remains incompletely characterized. This study assessed attitudes toward AI supported care among patients attending hospitals in three Jordanian governorates (Amman, Balqa, Irbid) and examined demographic and digital literacy correlates of acceptance. In a cross sectional survey (n = 500 complete questionnaires), participants rated exposure to AI in healthcare and five attitudinal domains, namely perceived usefulness or performance expectancy, trust and transparency, privacy and perceived risks, empathy and human interaction, and readiness or behavioral intention, using 25 items on 5 point Likert scales. Patients expressed conditional optimism: empathy and human interaction was most strongly endorsed (M = 4.33, SD = 0.58), alongside relatively high perceived usefulness (M = 3.97, SD = 0.68), while trust and transparency (M = 3.57, SD = 0.74) and readiness (M = 3.66, SD = 0.90) were moderate to high; privacy and risk concerns were moderate (M = 3.51, SD = 0.77) and self reported exposure was lowest (M = 2.57, SD = 1.07). The highest agreement item indicated preference for AI to work alongside physicians rather than be relied on alone (M = 4.47, SD = 0.81). Trust and transparency and perceived usefulness were positively associated with readiness (r = 0.48 and r = 0.44, respectively; p < .001), while privacy and perceived risks were negatively correlated with trust and usefulness. In multivariable regression adjusting for gender, age group, education, prior AI health app or device use, and self rated digital skill, lower educational attainment and self-rated digital skill were associated with readiness (R2 = 0.101). These findings suggest that implementation strategies in Jordan should emphasize human involvement alongside AI, transparent communication and governance, and attention to education- and digital-confidence-related differences in readiness.

Identifiers

PMID42424345
PMCPMC13349153

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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.