Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology
Apr 9, 2025Journal of translational medicine
Using Artificial Intelligence to Help Diagnose Digestive Tract Cancers: Opportunities and Challenges in Endoscopy and Tissue Analysis
Artificial intelligence has shown potential in significantly improving detection rates for malignant digestive tract tumors during endoscopic procedures.
Multiple models have improved real-time detection rates for polyps, early gastric cancer, and esophageal cancer.
Some AI systems have successfully progressed to clinical trials, indicating a step towards practical application.
The quality and scale of data collected across studies varies widely, impacting generalizability to different clinical settings.
AI methods in pathological analysis demonstrate capabilities in tissue segmentation, tumor grading, and assisted diagnosis.
Obstacles remain for clinical implementation, including standardization of data, lack of extensive validation, and challenges in model interpretability.
AI simplified
BACKGROUND: Malignant digestive tract tumors are highly prevalent and fatal tumor types globally, often diagnosed at advanced stages due to atypical early symptoms, causing patients to miss optimal treatment opportunities. Traditional endoscopic and pathological diagnostic processes are highly dependent on expert experience, facing problems such as high misdiagnosis rates and significant inter-observer variations. With the development of artificial intelligence (AI) technologies such as , real-time lesion detection with endoscopic assistance and automated pathological image analysis have shown potential in improving diagnostic accuracy and efficiency. However, relevant applications still face challenges including insufficient data standardization, inadequate interpretability, and weak clinical validation.
OBJECTIVE: This study aims to systematically review the current applications of artificial intelligence in diagnosing malignant digestive tract tumors, focusing on the progress and bottlenecks in two key areas: endoscopic examination and pathological diagnosis, and to provide feasible ideas and suggestions for subsequent research and clinical translation.
METHODS: A systematic literature search strategy was adopted to screen relevant studies published between 2017 and 2024 from databases including PubMed, Web of Science, Scopus, and IEEE Xplore, supplemented with searches of early classical literature. Inclusion criteria included studies on malignant digestive tract tumors such as esophageal cancer, gastric cancer, or colorectal cancer, involving the application of artificial intelligence technology in endoscopic diagnosis or pathological analysis. The effects and main limitations of AI diagnosis were summarized through comprehensive analysis of research design, algorithmic methods, and experimental results from relevant literature.
RESULTS: In the field of , multiple deep learning models have significantly improved detection rates in real-time polyp detection, early gastric cancer, and esophageal cancer screening, with some commercialized systems successfully entering clinical trials. However, the scale and quality of data across different studies vary widely, and the generalizability of models to multi-center, multi-device environments remains to be verified. In pathological analysis, using convolutional neural networks, multimodal pre-training models, etc., automatic tissue segmentation, tumor grading, and assisted diagnosis can be achieved, showing good scalability in interactive question-answering. Nevertheless, clinical implementation still faces obstacles such as non-uniform data standards, lack of large-scale prospective validation, and insufficient model interpretability and continuous learning mechanisms.
CONCLUSION: Artificial intelligence provides new technological opportunities for endoscopic and pathological diagnosis of malignant digestive tract tumors, achieving positive results in early lesion identification and assisted decision-making. However, to achieve the transition from research to widespread clinical application, data standardization, model reliability, and interpretability still need to be improved through multi-center joint research, and a complete regulatory and ethical system needs to be established. In the future, artificial intelligence will play a more important role in the standardization and precision management of diagnosis and treatment of digestive tract tumors.
Key numbers
98%
Sensitivity of AI for esophageal cancer detection
Achieved by a -based screening system.
2×
Miss rate reduction for colorectal tumors
AI technology reduced the miss rate compared to traditional methods.
85.3%
Diagnostic accuracy for early gastric cancer
Achieved by an AI detection system analyzing gastroscopy videos.
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