Evidence map›Paper›PMID 40234807›Full record

ArticleBMC cancer2025

Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.

Hein Minn Tun, Hanif Abdul Rahman, Lin Naing, Owais Ahmed Malik

Abstract readScoping Review
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  3. Review
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  7. Application of Cancer Screening Guidelines in Resource Limited Settings.Cancer control : journal of the Moffitt Cancer Center
    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

4 authors.

Hein Minn TunPAPRSB Institute of Health Science, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei. 23H8750@ubd.edu.bn.
Hanif Abdul RahmanPAPRSB Institute of Health Science, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei.
Lin NaingPAPRSB Institute of Health Science, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei.
Owais Ahmed MalikSchool of Digital Science, Universiti Brunei Darussalam, Lebuhraya Tungku, Bandar Seri Begawan, Brunei.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer remains a significant health challenge in the ASEAN region, highlighting the need for effective screening programs. However, approaches, target demographics, and intervals vary across ASEAN member states, necessitating a comprehensive understanding of these variations to assess program effectiveness. Additionally, while artificial intelligence (AI) holds promise as a tool for cancer screening, its utilization in the ASEAN region is unexplored. PURPOSE: This study aims to identify and evaluate different cancer screening programs across ASEAN, with a focus on assessing the integration and impact of AI in these programs.

methodsA scoping review was conducted using PRISMA-ScR guidelines to provide a comprehensive overview of cancer screening programs and AI usage across ASEAN. Data were collected from government health ministries, official guidelines, literature databases, and relevant documents. The use of AI in cancer screening reviews involved searches through PubMed, Scopus, and Google Scholar with the inclusion criteria of only included studies that utilized data from the ASEAN region from January 2019 to May 2024.

resultsThe findings reveal diverse cancer screening approaches in ASEAN. Countries like Myanmar, Laos, Cambodia, Vietnam, Brunei, Philippines, Indonesia and Timor-Leste primarily adopt opportunistic screening, while Singapore, Malaysia, and Thailand focus on organized programs. Cervical cancer screening is widespread, using both opportunistic and organized methods. Fourteen studies were included in the scoping review, covering breast (5 studies), cervical (2 studies), colon (4 studies), hepatic (1 study), lung (1 study), and oral (1 study) cancers. Studies revealed that different stages of AI integration for cancer screening: prospective clinical evaluation (50%), silent trial (36%) and exploratory model development (14%), with promising results in enhancing cancer screening accuracy and efficiency.

conclusionCancer screening programs in the ASEAN region require more organized approaches targeting appropriate age groups at regular intervals to meet the WHO's 2030 screening targets. Efforts to integrate AI in Singapore, Malaysia, Vietnam, Thailand, and Indonesia show promise in optimizing screening processes, reducing costs, and improving early detection. AI technology integration enhances cancer identification accuracy during screening, improving early detection and cancer management across the ASEAN region.

Indexed as

Artificial IntelligenceEarly Detection of CancerNeoplasmsAsia, SoutheasternHumansMass ScreeningArtificial intelligenceASEANCancer screening

Identifiers

PMID40234807
PMCPMC12001681

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.