Evidence map›Paper›PMID 40523280›Full record

ArticleJournal of medical Internet research2025

Insights Into the Current and Future State of AI Adoption Within Health Systems in Southeast Asia: Cross-Sectional Qualitative Study.

Mochammad Fadjar Wibowo, Alexandra Pyle, Emma Lim, Joshua W Ohde, Nan Liu, Jonas Karlström

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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.

Mochammad Fadjar Wibowo *SingHealth Duke-NUS Global Health Institute, Duke-NUS Medical School, Singapore, Singapore.ORCID https://orcid.org/0000-0002-1269-3081
Alexandra Pyle *Department of Future Health System, Singapore General Hospital, Singapore, Singapore.ORCID https://orcid.org/0000-0002-8960-2416
Emma LimGlobal Public Health, Global Studies, University of Virginia, Charlottesville, VA, United States.ORCID https://orcid.org/0009-0008-1393-1134
Joshua W OhdeCenter for Digital Health, Mayo Clinic, Rochester, MN, United States.ORCID https://orcid.org/0000-0002-4211-4490
Nan LiuCentre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.ORCID https://orcid.org/0000-0003-3610-4883
Jonas KarlströmSingHealth Duke-NUS Global Health Institute, Duke-NUS Medical School, Singapore, Singapore.ORCID https://orcid.org/0000-0002-7279-428X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) holds potential to enhance health systems worldwide. However, its implementation in health systems in Southeast Asia (SEA)-a region of diverse geopolitical and socioeconomic development-has been understudied.

objectiveThis study aims to gain insights into the current state and future prospects of AI technology from participants most directly involved in its adoption across health systems in SEA whose perspectives have received limited attention in research to date.

methodsWe used a cross-sectional qualitative research design. Data were collected through 31 semistructured interviews with participants working in or significantly involved with the implementation of AI-enabled technologies within health systems across 7 SEA countries: Brunei Darussalam, Indonesia, Myanmar, Singapore, Thailand, Vietnam, and the Philippines. The participants represented the public, private, and nonprofit sectors. They included innovators, health care professionals using AI, professionals from nongovernmental and multilateral organizations, corporate professionals, academics, policy makers, regulators, and investors. All interviews were audio recorded and transcribed verbatim. The collected data were then analyzed using thematic analysis methodology to identify key themes.

resultsOf the 31 participants, 8 (26%) were from lower-middle-income countries, 16 (52%) from upper-middle-income countries, and 7 (22%) from high-income countries. Through thematic analysis, five major categories emerged: (1) AI technology acceptance, (2) disparities in digital transformation, (3) technology governance, (4) data governance, and (5) AI for health system transformation. Participants discussed the promise of AI technology for adoption and integration in the health sector. In lower-middle-income and upper-middle-income countries, disparities in digital transformation-such as infrastructure barriers, market access concerns, and limited investment-were viewed as critical impediments. Across all country income levels, technology and data governance were considered essential for the ethical integration of AI into health care systems. AI is perceived to have the potential to transform health systems, including population health management, service accessibility, operations management, health systems financing and health care payment, and personalized medicine.

conclusionsOur study provides novel perspectives and valuable insights into the current state and future prospects of AI adoption across health systems in SEA. By capturing the experiences and opinions of a broad range of professionals involved in health care and AI, this research provides a nuanced understanding of the opportunities and hurdles associated with health AI in the region. For the full potential of AI-enabled technologies to be successfully implemented and ultimately contribute to the transformation of health systems in the region, foundational investments are needed in digital infrastructure, technology governance, and data governance. These fundamental pillars are crucial for fostering an environment in which AI can be effectively and ethically leveraged to improve health outcomes and strengthen health care systems throughout SEA.

Indexed as

Artificial IntelligenceDelivery of Health CareAsia, SoutheasternCross-Sectional StudiesHumansInterviews as TopicQualitative ResearchaccessibilityAIartificial intelligencedigital healthdigital transformationgovernancehealth system integrationSoutheast Asiatechnology adoption

Identifiers

PMID40523280
PMCPMC12209719

What OpenQuestion holds

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LicenceCC BY
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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.