Evidence map›Paper›PMID 42152022›Full record

ArticleTrials2026

Artificial intelligence-assisted integrated care to promote colonoscopy uptake (AICC) in China: study protocol for a cluster randomized controlled trial.

Xin Wang, Huilan Zhou, Nan Zhang, Yan Liu, Haipeng Wang, Jiewei Tang, Zhuoran Sun, Zhiyuan Hou

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in Trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07261059 (Artificial Intelligence-assisted Integrated Care to Promote Colonoscopy Uptake in China), which is not on this map. Not yet cited in PubMed.

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0citing papers in PubMed
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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.

NCT07261059 nanot yet recruitingnot on this map

Artificial Intelligence-assisted Integrated Care to Promote Colonoscopy Uptake in China: a Cluster Randomized Controlled Trial

TypeinterventionalSponsorFudan UniversityRan2025 to 2026Enrolled400ConditionsColorectal Neoplasms, ColonoscopyArmsAI-assisted integrated care
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

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5 · Who and what money

Authors and funding

8 authors.

Xin WangSchool of Public Health, Sun Yat-Sen University, Guangzhou, China.
Huilan ZhouSchool of Public Health, Sun Yat-Sen University, Guangzhou, China.
Nan ZhangShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Yan LiuShandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Haipeng WangDepartment of Social Medicine and Health Management, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Jiewei TangCollege of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China.
Zhuoran SunSchool of Public Health, Fudan University, Shanghai, China.
Zhiyuan HouSchool of Public Health, Fudan University, Shanghai, China. zyhou@fudan.edu.cn.

Funding

National Natural Science Foundation of China 72574046National Natural Science Foundation of China 72574131
6 · The paper itself

Abstract

backgroundColorectal cancer (CRC) leads to heavy disease and economic burdens globally. Early screening such as colonoscopy has been demonstrated to reduce both CRC incidence and mortality. However, uptake of colonoscopy among high-risk individuals is low in China, limiting screening efficacy. To improve screening uptake, it is necessary to ensure the engagement of both specialist and general practitioners. And recently, AI techniques show considerable promise in delivering personalized health education. This study aims to evaluate the effectiveness of AI-assisted integrated care, compared with specialty care in improving uptake of colonoscopy among CRC high-risk individuals in China.

methodsA two-arm, parallel cluster randomized controlled trial will enroll 400 high-risk residents for CRC aged 40-64 years from 40 communities/villages across one urban district and two rural counties in Shandong Province, China. Communities/villages as clusters will be randomized within counties to either a specialty care group or an AI-assisted integrated care group. Participants will be those initially identified as high risk for CRC through a risk assessment questionnaire or fecal immunochemical test. The primary outcomes are colonoscopy uptake at 3 and 6 months post-intervention and the time to colonoscopy. Secondary outcomes encompass CRC screening literacy, beliefs, and intention to undergo colonoscopy. Additionally, we will also conduct a process evaluation and health economic evaluation. DISCUSSION: This study proposes a novel strategy integrating a multidisciplinary team with digital health solutions. We expect that this strategy will provide a feasible approach to improve colonoscopy uptake. The findings could offer practical insights for informing and advancing cancer prevention and control initiatives in China.

trial registrationClinicalTrial.gov NCT07261059 . Registered on December 2, 2025.

Indexed as

Artificial IntelligenceColonoscopyColorectal NeoplasmsDelivery of Health Care, IntegratedEarly Detection of CancerPatient Acceptance of Health CareAdultChinaFemaleHealth Knowledge, Attitudes, PracticeHumansIntelligent SystemsMaleMiddle AgedPredictive Value of TestsRandomized Controlled Trials as TopicArtificial intelligenceColonoscopyColorectal cancerIntegrated careTrial

Identifiers

PMID42152022
PMCPMC13347978

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Registered trials

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.