Evidence map›Paper›PMID 40198603›Full record

ArticlePLOS digital health2025

Navigating digital frontiers in UAE healthcare: A qualitative exploration of healthcare professionals' and patients' experiences with AI and telemedicine.

Azza Alkaabi, Deena Elsori

Abstract read
In one paragraph

Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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  5. Review
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

2 authors.

Azza AlkaabiDepartment of Student Affairs, Rabdan Academy, Abu Dhabi, United Arab Emirates.ORCID https://orcid.org/0009-0000-3827-1808
Deena ElsoriDepartment of Student Affairs, Rabdan Academy, Abu Dhabi, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence (AI) and telemedicine into healthcare has significantly advanced patient-centered care, enhancing accessibility, convenience, and patient-doctor relationships. However, different factors determine the extent to which such benefits are realized, especially in unique healthcare settings such as the United Arab Emirates (UAE). In this regard, this research explores healthcare professionals' and patients' perspectives to understand various factors that influence the adoption and use of AI in the UAE's healthcare sector. This research sought to understand the benefits, challenges, and enablers of successful adoption and utilization of AI and telemedicine in the UAE's healthcare setting. Through this objective, this research aims to contribute to the scanty knowledge on the integration of emerging technologies, such as AI, in different infrastructural and cultural contexts. The study employed a qualitative approach, through which eight healthcare professionals and seven patients (totaling 15 participants) were recruited from Dubai- and Abu Dhabi-based hospitals using the purposive sampling strategy. The participants' insights and views on the research topic were captured using semi-structured face-to-face interviews. These interviews were analyzed using the thematic analysis strategy. This study established that while AI and telemedicine are associated with various benefits, including enhancing the management of chronic illnesses, effective controlling of infectious diseases, saving patients and hospitals health-related costs and time, and enhancing convenience, they suffer from various drawbacks, including limited infrastructural and financial resources, significant gaps in skills, safety concerns, and the likelihood of misdiagnosis and misinformation. The study also observed that the successful integration of AI and telemedicine in the UAE healthcare sector necessitated incentivizing stakeholders to use this technology, full involvement and engagement of stakeholders across all stages of implementation, adequate training of the healthcare staff, and public engagement and awareness. This research demonstrates that integrating AI and telemedicine in the UAE healthcare sector necessitates addressing contextual infrastructural and cultural hindrances. The results highlight the need to address such limitations, adequately train healthcare professionals, and enhance data privacy. The study also lays a foundation for further research into contextual challenges hindering the effective adoption and implementation of AI and telemedicine in different healthcare settings in order to develop a generic, context-specific framework that will guide the adoption of such emerging technologies in the global healthcare industry.

Identifiers

PMID40198603
PMCPMC11991283

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Registered trials

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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.