<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JCER</journal-id><journal-title-group><journal-title>Journal of Contemporary Educational Research</journal-title></journal-title-group><issn>2208-8466</issn><eissn>2208-8474</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jcer.v9i12.13442</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Application Effect of Artificial Intelligence-Assisted Diagnostic System in the Clinical Diagnosis and Treatment of Digestive System Tumors under the Background of Interdisciplinary Integration of Medicine and Engineering</title><url>https://artdesignp.com/journal/JCER/9/12/10.26689/jcer.v9i12.13442</url><author>ChangLe,ChangChen</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>9</volume><issue>12</issue><history><date date-type="pub"><published-time>2025-12-31</published-time></date></history><abstract>Objective: To explore the application effect of an artificial intelligence (AI)-assisted diagnostic system in the clinical diagnosis and treatment of digestive system tumors (including gastric cancer, colorectal cancer, liver cancer, etc.), providing evidence-based support for improving the efficiency and accuracy of diagnosis and treatment of digestive system tumors. Methods: A total of 200 patients with digestive system tumors admitted to our hospital from January 2022 to December 2024 were selected and divided into an observation group (n = 100, receiving AI-assisted diagnosis combined with conventional diagnosis and treatment) and a control group (n = 100, receiving conventional diagnosis and treatment) according to the random number table method. The differences in diagnostic and treatment indicators between the two groups were compared. Results: The diagnostic time in the observation group was 18.25 ± 3.68 minutes, significantly shorter than that in the control group (35.72 ± 5.14 minutes) (t = 25.36, P &amp;lt; 0.001). The missed diagnosis rate and misdiagnosis rate in the observation group were 2.00% (2/100) and 1.00% (1/100), respectively, significantly lower than those in the control group [8.00% (8/100) and 7.00% (7/100), respectively] (χ2 = 4.01, 5.03, both P &amp;lt; 0.05). Conclusion: The AI-assisted diagnostic system can significantly improve the diagnostic accuracy of digestive system tumors, shorten the diagnostic and treatment time, and reduce the missed diagnosis and misdiagnosis rates, demonstrating high application value in the clinical diagnosis and treatment of digestive system tumors.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Cai Y, 2024, Research on Medical Expenses, Service Utilization, Survival, and Their Relationships Among Hospitalized Patients with Malignant Tumors in China, dissertation, Jilin University.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Guo X, Jiang Z, 2022, New Advances in the Diagnosis and Treatment of Lung Cancer, Northwest University Press, 404.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Chai S, Huo B, Huo X, 2024, Radiation Particle Therapy for Thoracic Tumors, People’s Medical Publishing House, 819.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Wen C, 2023, Construction and Clinical Application Research of an Artificial Intelligence Diagnostic Model for Breast Cancer Based on Contrast-Enhanced Mammography, dissertation, Southern Medical University.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Xu W, Li B, Xu Y, et al., 2021, Establishment and Clinical Testing of an Automatic Recognition Model for Gastroscopic Images of Atrophic Gastritis-Gastric Cancer Based on a Fast Regional Convolutional Neural Network. 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