Evidence map›Paper›PMID 42129441›Full record

ArticleNPJ digital medicine2026

Global physician perspectives on artificial intelligence in healthcare across 50 countries and territories.

Bayarbaatar Bold, Oguzhan Serin, Lukitaningrum Tantri Adhiatma, Suvd-Erdene Demberel, Choni Wangmo, Ristania Ellya John, Luis Daniel Gatti Pineda, Chitsanupong Ratarat, Nisachon Tongtip, Mohammed Shakeebuddin Kashif and 10 more

Abstract read
In one paragraph

Article in NPJ digital 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
–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

20 authors.

Bayarbaatar Bold *Mongolian Society of Artificial Intelligence in Medicine, Ulaanbaatar, Mongolia. bayarbaatar99@gmail.com.
Oguzhan Serin *University of Bristol, Bristol, UK.
Lukitaningrum Tantri AdhiatmaQueen Mary University of London, London, UK.
Suvd-Erdene DemberelUniversity of Warwick, Coventry, UK.
Choni WangmoUniversity College London, London, UK.
Ristania Ellya JohnUniversity College London, London, UK.
Luis Daniel Gatti PinedaUniversity of Aberdeen, Aberdeen, UK.
Chitsanupong RataratUniversity College London, London, UK.
Nisachon TongtipUniversity of Birmingham, Birmingham, UK.
Mohammed Shakeebuddin KashifKidwai Memorial Institute of Oncology, Bengaluru, India.
Nicole Kessa WeeNational Healthcare Group, Singapore, Singapore.
Farhana FadzliUniversity Malaya Medical Centre, Kuala Lumpur, Malaysia.
Urooj KanwalJinnah Postgraduate Medical Center, Karachi, Pakistan.
Trang Ngọc NguyễnBachmai University Hospital, Hanoi, Vietnam.
Bunta TokudaKyoto Prefectural University of Medicine, Kyoto, Japan.
Weiyi LiUniversity of Bristol, Bristol, UK.
Phathayphout PhetvilayDiagnostic Radiology and Radiotherapy Centre , Mittaphab Hospital, Ministry of Health, Vientiane, Laos.
Naw Paw Say WahYangon General Hospital, University of Medicine, Yangon, Myanmar.
Ravinath KannangaraUniversity of Bristol, Bristol, UK.
Priscilla Akyaa Kyei-BaffourUniversity of Ghana Medical Center, Accra, Ghana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite rapid advances in artificial intelligence (AI) capabilities, clinical integration remains limited, partly due to an incomplete understanding of physicians' real-world engagement and determinants of adoption. We conducted an international cross-sectional survey between June and September 2024 using a 30-item questionnaire translated into 13 languages, analysing 1049 complete responses from 50 countries and territories. Most respondents reported fundamental to advanced understanding of AI (86.5%) and believed it would improve clinical practice (80.2%), particularly in efficiency (53.5%), timeliness (52.0%), and effectiveness (44.0%). However, only 27.8% had used AI in practice, and 17.7% had received formal training. Mann-Whitney analyses showed that positive attitudes and formal training were associated with greater AI understanding, familiarity and usage (all p ≤ 0.008). In multivariable regression, physicians with formal training were more than three times as likely to use AI (adjusted OR 3.40; 95% CI 2.31-5.01; p < 0.001), and those working in institutions with AI technologies were more than eight times as likely (adjusted OR 8.43; 95% CI 5.74-12.34; p < 0.001). These findings indicate a persistent gap between awareness and adoption driven primarily by structural factors, particularly the absence of formal AI training and limited institutional investment in AI technologies.

Identifiers

PMID42129441
PMCPMC13402749

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.