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

BMC Cancer volume 25, Article number: 703 (2025) Cite this article

Abstract

Background

Cancer 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.

Methods

A 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.

Results

The 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.

Conclusion

Cancer 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.

Peer Review reports

Background

According to the International Agency for Research on Cancer, one in five people worldwide will develop cancer during their lifetime which leads to cancer prevention as one of the most significant public health challenges of the twenty-first century [1]. Cancer patients who experience just a four-week delay in treatment face a 6–13% increased risk of mortality [2]. 40% of all cancer cases could potentially be prevented through primary preventive measures, indicating that effective cancer screening programs can play a crucial role in early detection and treatment, as well as reducing cancer mortality [3]. A few types of cancers are recommended for screening based on the life-years gained, the availability of diagnosis and treatment, the benefits outweighing the associated harms such as overdiagnosis and overtreatment, and the benefits of cost -effectiveness [4]. Furthermore, continuous monitoring and evaluation of cancer screening programs are essential to ensure benefits are achieved, harms are minimized, efficiency and cost-effectiveness are maintained based on the changing situations [5].

In the Association of Southeast Asia Nations (ASEAN) region, cancer remains one of the most pressing health challenges with the incidence rates steadily rising over the years with the increase aging population, increased risk behavior and environmental factors such as climate change. In addition, the diverse demographics, socioeconomic factors, and healthcare infrastructures across ASEAN member states contribute to variations in cancer prevalence and mortality rates in the region [1, 3, 6]. According to the ASEAN Costs in Oncology (ACTION) study group, over 75% of cancer patients either experienced death or financial catastrophe within a year of diagnosis, with higher risk for vulnerable groups from socioeconomically disadvantaged cancer patients and those diagnosed at advanced stages [6]. The World Health Organization (WHO) has published cancer screening guidelines and recommendations for common types of cancer such as cervical, breast and colorectal cancer [7]. Based on policy and economic priorities for cancer prevention, each ASEAN member country has notable differences in cancer screening program designs, organizational structures, target groups and intervals for screening [8].

Artificial Intelligence (AI) technology has shown promising results in oncology, including cancer screening, early detection, diagnosis, treatment, and prognosis prediction. With the improvement of digital infrastructure together with high accuracy algorithms and the availability of enormous data, the AI models are becoming more capable of advancing cancer screening programs [9]. Furthermore, AI’s integration into cancer screening promises early detection through radiology images such as chest X-ray and Low-Dose Computed Tomography (LDCT) for lung cancer, mammography for breast cancer, cervical cancer detection via visual inspection with acetic acid (VIA), prostate-specific antigen (PSA) detection for prostate cancer and colon cancer detection via classification of Fecal Immunochemical Tests (FIA) [10,11,12,13,14]. However, challenges remain, including the variability in the healthcare infrastructure and governance policy, lack of standardized protocols, concerns related to data privacy and algorithmic biases [9, 15].

Despite the promise of AI in revolutionizing cancer screening, its utilization and integration in the ASEAN region is underexplored. ASEAN region where healthcare resources and accessibility vary diversely, AI could provide a bridge to narrow the gap between the high demand and limited medical expertise. In rural areas with limited specialists, AI-integrated tools can provide support to non-specialist healthcare providers in the early detection of cancer and facilitate large-scale screening programs by automating routine tasks and making more efficient use of medical resources [16]. The aim of this review paper is to explore the current landscape of different screening programs in the ASEAN region and identify the potential utilization of AI technology in cancer screening programs. The result of the study will further provide support to different levels of stakeholders, including policymakers, healthcare workers, and researchers, to explore the integration of AI in cancer screening to reduce the mortality and economic burden of cancer in the region.

Methods

We conducted literature research for the identification of the existing landscape of cancer screening programs in the ASEAN region through data collected from government health ministries, official guidelines, literature databases, and relevant documents. Then we conducted a scoping review on the utilization of AI technology in the ASEAN region following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines [17].

Type of study and information sources

We conducted a scoping review of articles published between January 1, 2019, and May 31, 2024, following the Arksey and O'Malley methodological framework [18]. Our sources included PubMed, Scopus, and Google Scholar. We used a combination of English keywords such as "cancer screening" or "cancer early detection " or “cancer prevention” alongside terms like "artificial intelligence” or " machine learning" or " deep learning " and "ASEAN" or "Indonesia" or "Malaysia" or "Philippines" or "Thailand" or "Vietnam" or "Laos" or "Myanmar" or "Brunei Darussalam" or "Cambodia" or "Timor-Leste". Additionally, we employed the snowball strategy to identify further sources from the references of relevant full texts. Both MeSH (Medical Subject Headings) terms and free-text terms were utilized in the search.

Eligibility criteria

We included research articles explicitly describing the use of AI in cancer screening within the ASEAN region. We only included those studies that specifically mention the use of AI in cancer screening and data utilized exclusively from ASEAN member states. We excluded the articles which did not utilize the data from the ASEAN region to ensure the relevance of findings to our target demographics. Non-peer-reviewed articles, editorials, opinion pieces, and other non-research literature were also excluded to maintain the scientific credibility of our review.

Data extraction

Two researchers initially screened titles and abstracts to determine if they met the inclusion. After removing duplicates, full texts were reviewed to identify potential exclusion criteria. Any disagreements regarding eligibility criteria were resolved through discussion with another team member. The Mixed Methods Appraisal Tool (MMAT) was used for this purpose, allowing for the evaluation of studies with diverse methodologies [19]. Data were extracted based on author, country of data used, study design and type of AI method used. We further extracted the outcome measure for each study focusing on evaluation matrices of AI such as specificity, sensitivity, precision, positive predictive value, accuracy, recall, ROC (receiver operating characteristic curve), AUC (area under curve), F1/F2 score, and detection rate based on the type of the study. We did not conduct an ad hoc risk-of-bias assessment for the studies included. The quality of the available literature was assessed by the match between the study objective and results.

Data synthesis and analysis

The data were organized into an evidence matrix using a standardized template in Google Sheets. AI applications for systemic review data extraction such as Zotero 6, Pop AI, and Rayyan were utilized to analyze the abstracts of 41 relevant studies after applying exclusion criteria. The data charter was created to display the author, the year of publication, data used from which ASEAN country, the study title accompanied by the study design and the type of AI method used in the study. Additionally, information related to evaluation matrices was included in the data charter. Further analyses were conducted on the stage of AI integration in healthcare: (1) exploratory model development, (2) a silent trial, and (3) prospective clinical evaluation and meta-analysis on the evaluation matrices [20, 21].

Results

Overview of cancer screening programs in ASEAN

Table 1 indicates the ASEAN region's cancer screening programs, which indicate diverse program designs and methods for different cancers. Cervical cancer screening is the most prevalent type of screening program in the ASEAN region with all countries aiming to implement opportunistic and organized (systematic, population-based initiatives) approaches. Brunei Darussalam established organized mammography screenings for breast cancer since 2008 for women aged 40–60 every 3 years, followed by opportunistic screenings for cervical cancer (Pap smears and HPV testing alone or contesting for individuals aged 20–29 and 30–65 at intervals of 3 and 5 years, respectively initiated since 2010), colorectal cancer screening (Fecal immunochemical tests (FIT) and colonoscopy for individuals aged 50–75 every 2 years since 2012), Nasopharyngeal and prostate cancer.

Table 1 Overview of all cancer screening programs in the ASEAN region until May 2024
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Singapore has established organized programs for breast, cervical, and colorectal cancers with mammography for women aged 50–69 every 2 years since 2002, Pap smears for women aged 25–69 at intervals of 3 and 5 years, and colorectal screening with FIT and colonoscopy annually since 2011. Malaysia combines opportunistic and organized screenings, offering clinical breast examinations (CBE) and mammography for women aged 35 and above, with screening intervals of 1 and 2 years, respectively, and cervical cancer screenings with Pap smears every 3 years since 1995. Colorectal cancer screenings with FIT have been conducted annually for individuals aged 50 and above since 2014. Furthermore, Thailand’s organized cervical cancer screening program offers Pap smears with limited geographic area every 5 years for women aged 30–60, and colorectal cancer screening with FIT for individuals aged 50–70 every 2 years since 2018.

The rest of the countries in the region primarily implement opportunistic cancer screening, relying on individual healthcare provider recommendations, which often target high-risk populations rather than systematic public health initiatives [7]. In both limited rural and urban areas, Myanmar offers visual inspection with acetic acid (VIA) and HPV DNA testing for cervical cancer targeting HIV-positive individuals of reproductive age and HIV-negative individuals aged 30–49. Both Cambodia and Laos use opportunistic screening for cervical cancer. In Cambodia, HPV DNA testing for HIV-positive individuals and pregnant women began in 2020, while in Laos, VIA, cytology, and tests for HPV DNA have been used on a case-by-case basis since 2021. Since 2015, Indonesia has provided opportunistic mammography-based breast cancer screening for women aged 40–74 every two years, as well as HPV DNA testing and cytology-based cervical cancer screening for women aged 30–69 every ten years. The Philippines also implements opportunistic screening methods, offering mammography for breast cancer screening and VIA triage as Pap tests for cervical cancer screening to women between the ages of 25 and 55 every five to seven years. Vietnam's opportunistic programs target women aged 30–54 and include Pap smears for cervical cancer and mammography for breast cancer. Timor Leste announced in 2024 that opportunistic cervical cancer screening involves VIA and colposcopy for women between the ages of 35 and 45.

Selection of source of evidence

The article selection process involved two selection phases: [1] title and abstract review, and [2] full‐text review. Figure 1 depicts the flow diagram of the study selection process. Initially, 781 records were identified through searches in three databases: PubMed (56 records), Scopus (23 records), and Google Scholar (702 records). These records underwent a screening process by an independent reviewer to remove duplicates and articles that were not relevant based on their titles and abstracts, resulting in 302 records being retained for further review. Subsequent screening excluded 261 articles unrelated to cancer screening in the ASEAN region or had non-retrievable information. This narrowed the pool down to 41 articles that were accessed for eligibility [41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81]. Finally, with the final discussion by the reviewer, an in-depth text analysis was conducted, leading to the exclusion of an additional 27 articles that did not utilize data from the ASEAN region, culminating in 14 studies being included in the final analysis.

Fig. 1
figure 1

Flow chart for study selection process

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Use of artificial intelligence in cancer screening program

Characteristics of included studies

Fourteen studies were included in this review, covering various cancer types: breast (n = 5), cervical (n = 2), colon (n = 4), hepatic(n = 1), lung(n = 1), and oral (n = 1) cancers which as described in Fig. 2. The studies spanned from 2019 to May 2024, with six published in 2023, four up to May 2024, two in 2022, and one each in 2021 and 2019. Geographically, 29% of the studies utilized data from Malaysia, 21% from Thailand, 22% from Singapore and, 14% each from Indonesia and Vietnam. Regarding AI integration stages, 50% of the studies were in the prospective clinical evaluation or supportive clinical decision-making stage, 36% were in the silent trial stage, and 14% were in the exploratory model development stage.

Fig. 2
figure 2

Distribution of the reviewed studies focusing on the utilization of AI technology in the ASEAN region until May 2024

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Table 2 shows that Sachithanandan et al. (2024) used data from Malaysia to create an algorithm for lung cancer screening that combined deep learning and machine learning with assisted chest radiography [82]. Using data from Vietnam, Pham et al. (2023) used a multimodal approach combining genome-wide methylation changes for early-stage breast cancer detection [83]. Le et al. (2024) classified breast cancer X-ray images using transfer learning with deep convolutional neural networks, namely the Residual Network (ResNet 34), utilizing data from Vietnam [84]. While Nabheerong et al. (2023) used data from Thailand to apply the inertial relaxed CQ algorithm for breast cancer screening, Hanis et al. (2022) used a machine learning algorithm in a retrospective study using patient registration records from Malaysia [85, 86]. Using data from Malaysia, Hamid et al. (2024) carried out a retrospective cross-sectional study for breast cancer screening utilizing the Lunit INSIGHT MMG system, while Mohamad Marzuki et al. (2019), who created an AI-integrated mobile app for colorectal cancer education using data from Malaysia [87, 88].

Table 2 Characteristics of the reviewed studies focusing on the utilization of AI technology in the ASEAN region until May 2024
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Koh et al. (2023) and Chin et al. (2023), both conducted prospective cohort studies using real-time AI-aided endoscopy to improve adenoma detection rates with data from Singapore and Lim et al.(2024), used contextual knowledge of chat GPT 4.0 to provide on appropriate colonoscopy intervals with comparative study design using hypothetical patient scenarios [89,90,91]. Utilizing data from Indonesia, Harsono et al. (2022) created an AI-based application for the detection of cervical precancerous lesions, and Nurmaini et al. (2023) assessed an AI-assisted cervicography interpretation system [92, 93]. Warin et al. (2021) carried out a retrospective analysis using a deep learning model to classify oral cancer from images with data from Thailand, while Tiyarattanachai et al. (2023) conducted a prospective randomized controlled study on AI-assisted detection of focal liver lesions during ultrasound examinations using data from Thailand [94, 95].

Performance of AI in the reviewed studies

The outcome indicators for each study vary based on the design and purpose of the studies which is described in Table 3. Sachithanandan et al. developed a lung cancer screening algorithm in Malaysia with a focus on risk stratification while analyzing the outcomes from other studies in the articles. While in Vietnam, Pham et al. improved early-stage breast cancer detection with a sensitivity of 65% and specificity of 96%, achieving an AUC of 0.91, Le et al. classified breast cancer X-ray images into three breast reporting using the transfer learning method, achieving an AUC of 0.76609, a sensitivity of 58.5%, and a specificity of 79.42%. Using an algorithm, Nabheerong et al. in Thailand were able to achieve 82.56% precision, 85.03% accuracy, and 87.65% recall in mammography screening. Furthermore, an over-the-counter screening robust model for breast cancer was developed by Hanis et al. in Malaysia with sensitivities and specificities ranging from 0.76 to 0.82 and 0.79 to 0.88, respectively across multiple models. Hamid et al. in Malaysia improved breast lesion diagnosis through AI, demonstrating an increase in accuracy (AUC of 0.925) of detecting cancer using AI algorithm. In addition, Mohamad Marzuki et al. in Malaysia investigated the use of a mobile app as a chat and analyzer for colorectal cancer education.

Table 3 Characteristics and outcome indicators for the reviewed studies focusing on the utilization AI technology in the ASEAN region until May 2024
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In Singapore, Koh et al. used artificial intelligence to increase the percentage of adenoma detected during colonoscopy from 24.3% to 30.4%, while Chin et al. (2023) describe that AI-aided colonoscopies significantly increased polypectomy rates (33.6%) compared to non-AI-aided colonoscopies (28.4%) and improved polyp detection (PDR 45.6%) and adenoma detection rates (ADR 32.4%). Additionally, Lim et al. (2024) from Singapore using ChatGPT-4 accurately provided appropriate colonoscopy intervals based on patient history and guidelines, with no high-risk features missed. In Indonesia, Harsono et al. and Nurmaini et al. developed and evaluated AI applications for cervical cancer screening, achieving sensitivities of 80% and 99.49%, and precisions of 80% and 73%, respectively. The focal liver lesion detection rates during ultrasound exams were improved by Tiyarattanachai et al. in Thailand; non-expert detection rates with AI assistance were 36.9% as opposed to 21.4% without AI. Finally, Warin et al. in Thailand created an automated system for classifying oral cancer that achieved a sensitivity of 98.75%, specificity of 100%, and AUC of 0.99, proving that the Dense Net algorithm is highly effective in assisting with early diagnosis.

Discussion

From this following scoping review, we explore the diverse landscape of cancer screening programs in the ASEAN region. The age groups and intervals for cancer screening varied significantly throughout the region. Moreover, population-based organized and opportunistic methods are prioritized by Brunei Darussalam, Singapore, Malaysia, and Thailand to improve coverage of early cancer detection. In contrast, opportunistic screening is primarily used in Indonesia, the Philippines, Vietnam, Myanmar, Cambodia, Laos, and Timor Leste. Although all member states in the ASEAN region conducted cervical cancer screening programs using less expensive Visual inspection with acetic acid (VIA)test to more costly Papanicolaou (Pap smear) test, achieving WHO's 70% screening goal for ages 35–45 remains challenging due to resource constraints in many ASEAN nations [96].

Furthermore, it is recommended that ASEAN countries prioritize well-organized, population-based cancer screening, as these not only facilitate increased coverage but also guarantee equity and accessibility for all eligible members of the targeted population [97, 98]. With an ageing population and rising cancer risks, tailored strategies, improved healthcare infrastructure, and cohesive policies are crucial to enhance regional cancer outcomes. With an ageing population and rising cancer risks, tailored strategies, improved healthcare infrastructure, community cancer screening education and cohesive policies are crucial to enhance regional cancer outcomes.

The review highlighted the use of AI technology for common cancer screening programs in Singapore, Malaysia, Vietnam, Thailand, and Indonesia. The various levels of AI integration noted in the region have reached the stage of developing algorithms for lung and breast cancer, deploying AI models in real clinical settings for breast and oral cancer, and integrating AI into clinical decision-making for breast, liver, and cervical cancer. Although there was diversity in outcome measures, all studies discussed the potential for better detection of cancer in screening with the use of AI models together with professionals. Most of the models in the studies were either supervised or unsupervised machine learning and deep learning techniques. With the development of large language models such as ChatGPT, there are more opportunities to utilize AI in cancer screening. Existing projects, such as Color Health from OpenAI partnering with USF HDFCC (the University of California, San Francisco Helen Diller Family Comprehensive Cancer Center), are creating personalized cancer screening plans for high-risk patients [99].

To further understand the potential and challenges of AI-integrated cancer screening programs in the ASEAN region, a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis is conducted from this scoping review which was described in Table 4. One of AI's strengths is that it can improve patient management by minimizing needless interventions and giving precise diagnostic information. This is especially useful when combined with knowledgeable specialists, as this combination performs better than individual efforts in following recommended guidelines. AI-integrated systems work as a "second radiologist," assisting in the early diagnosis of breast cancer, while AI-assisted chest radiography expands public screening uptake and reaches low-risk populations. Breast clinic patient flow is enhanced by over-the-counter screening models, and underprivileged and remote populations benefit from AI algorithms in smartphone applications that detect precancerous lesions of cervical cancer images via VIA in real-time.

Table 4 SWOT analysis of the utilization of AI technology in the cancer screening program in the ASEAN region
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However, weaknesses such as inconsistent views on AI adoption among patients and clinicians, variability in AI-assisted screening reproducibility, and reliance on retrospective analysis pose integration challenges. Significant worries include algorithmic bias, especially in commercially trained AI software, and the possibility of false positive results leading to unnecessary investigation in settings with limited resources. Opportunities lie in using AI to extend early detection and intervention to underserved populations, yet threats include inconsistent AI governance and ethical issues across the ASEAN region, necessitating cohesive policies to address accountability, transparency, privacy, and diagnostic bias.

Our scoping review has several strengths that provide a comprehensive understanding of the benefits and challenges of AI technology implementation in the ASEAN region. First, our extensive search strategy across multiple databases ensured that we captured a wide range of studies on the topic. Second, we incorporated studies with distinctive designs, which gave us a better understanding of the practical and technical effects of integrating AI into cancer screening initiatives. Finally, we discussed the SWOT analysis to provide a more comprehensive review of the potential implications of integrating AI into screening programs.

Our scoping review has several limitations. The included studies were heterogeneous, preventing a quantitative synthesis of the evaluation indicators of AI solutions. Although summarizing the impact of AI on cancer screening can be useful, the lack of a quantitative synthesis (i.e., lack of a meta-analysis) limits the strength of our conclusions. Additionally, due to the inclusion of only English-language studies, publication bias may also have occurred. Future research should address these limitations by including more diverse languages and focusing on specific study designs to analyze the outcome measure.

Conclusion

The scoping review highlighted the diverse landscape and significant potential of AI-integrated cancer screening programs in the ASEAN region. The review highlights the need for well-organized, population-based approaches for cancer screening in ASEAN regions to meet WHO's 2030 screening targets of 70% of women being screened with a high-performance test by age 35 and again by age 45 for cervical cancer. AI technology shows promise in enhancing cancer screening accuracy, patient management, community awareness, and early detection in remote areas which can reduce costs, especially when combined with skilled specialists. However, challenges such as patient and clinician perceptions, algorithmic biases, and the need for prospective validation studies persist. Opportunities lie in extending AI benefits to underserved and remote populations, yet governance and ethical issues require cohesive policies across the region. To maximize AI's potential in cancer screening in the ASEAN region, it is essential to establish strong governance guidelines focused on the ethical use of AI. Future research should explore diverse study designs using various AI models and regional datasets to ensure comprehensive and equitable advancements in cancer screening.

Data availability

This study is a scoping review and relies solely on publicly available data from published literature, reports, and databases. No primary data were collected or generated as part of this research. All sources used in the study are appropriately cited, and the full list of references is provided within the manuscript.

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Authors and Affiliations

  1. PAPRSB Institute of Health Science, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei

    Hein Minn Tun, Hanif Abdul Rahman & Lin Naing

  2. School of Digital Science, Universiti Brunei Darussalam, Lebuhraya Tungku, Bandar Seri Begawan, Brunei

    Hein Minn Tun, Hanif Abdul Rahman & Owais Ahmed Malik

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  1. Hein Minn Tun
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  2. Hanif Abdul Rahman
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  3. Lin Naing
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Contributions

H.M.T conceptualized and wrote the manuscript text for 1st draft including all figures and tables. The scoping review paper selection was conducted by H.M.T and H.A.R, while O.A.M reviewed the process. H.A.R, O.A.M and L.N were included in validation of study data, supervision and editing of the Manuscript including figures and tables.

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Correspondence to Hein Minn Tun.

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Tun, H.M., Rahman, H.A., Naing, L. et al. Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review. BMC Cancer 25, 703 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-14026-x

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-14026-x

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BMC Cancer

ISSN: 1471-2407

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