Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology
Abstract 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 high...
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2025-04-01
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| Online Access: | https://doi.org/10.1186/s12967-025-06428-z |
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| author | Yinhu Gao Peizhen Wen Yuan Liu Yahuang Sun Hui Qian Xin Zhang Huan Peng Yanli Gao Cuiyu Li Zhangyuan Gu Huajin Zeng Zhijun Hong Weijun Wang Ronglin Yan Zunqi Hu Hongbing Fu |
| author_facet | Yinhu Gao Peizhen Wen Yuan Liu Yahuang Sun Hui Qian Xin Zhang Huan Peng Yanli Gao Cuiyu Li Zhangyuan Gu Huajin Zeng Zhijun Hong Weijun Wang Ronglin Yan Zunqi Hu Hongbing Fu |
| author_sort | Yinhu Gao |
| collection | DOAJ |
| description | Abstract 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 deep learning, 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 endoscopy, 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. Graphical Abstract |
| format | Article |
| id | doaj-art-e05badf497bb41cb825b01e35df0c599 |
| institution | DOAJ |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
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| series | Journal of Translational Medicine |
| spelling | doaj-art-e05badf497bb41cb825b01e35df0c5992025-08-20T03:06:52ZengBMCJournal of Translational Medicine1479-58762025-04-0123111810.1186/s12967-025-06428-zApplication of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathologyYinhu Gao0Peizhen Wen1Yuan Liu2Yahuang Sun3Hui Qian4Xin Zhang5Huan Peng6Yanli Gao7Cuiyu Li8Zhangyuan Gu9Huajin Zeng10Zhijun Hong11Weijun Wang12Ronglin Yan13Zunqi Hu14Hongbing Fu15Department of Gastroenterology, Shaanxi Province Rehabilitation HospitalDepartment of General Surgery, Changzheng Hospital, Navy Medical UniversityShanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of MedicineDivision of Colorectal Surgery, Changzheng Hospital, Navy Medical UniversityDepartment of Gastroenterology, Changzheng Hospital, Naval Medical UniversityDepartment of Gastrointestinal Surgery, Changzheng Hospital, Navy Medical UniversityDivision of Colorectal Surgery, Changzheng Hospital, Navy Medical UniversityInfection Control Office, Shaanxi Province Rehabilitation HospitalDepartment of Radiology, The First Hospital of Nanchang, the Third Affiliated Hospital of Nanchang UniversityTongji University School of Medicine, Tongji UniversityDepartment of General Surgery, Changzheng Hospital, Navy Medical UniversityTongji University School of Medicine, Tongji UniversityDepartment of Gastrointestinal Surgery, Changzheng Hospital, Navy Medical UniversityDepartment of Gastroenterology, Changzheng Hospital, Naval Medical UniversityDepartment of Gastrointestinal Surgery, Changzheng Hospital, Navy Medical UniversityDepartment of Gastrointestinal Surgery, Changzheng Hospital, Navy Medical UniversityAbstract 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 deep learning, 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 endoscopy, 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. Graphical Abstracthttps://doi.org/10.1186/s12967-025-06428-zMalignant digestive tract tumorsEndoscopyPathologyArtificial intelligenceDeep learningDiagnosis |
| spellingShingle | Yinhu Gao Peizhen Wen Yuan Liu Yahuang Sun Hui Qian Xin Zhang Huan Peng Yanli Gao Cuiyu Li Zhangyuan Gu Huajin Zeng Zhijun Hong Weijun Wang Ronglin Yan Zunqi Hu Hongbing Fu Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology Journal of Translational Medicine Malignant digestive tract tumors Endoscopy Pathology Artificial intelligence Deep learning Diagnosis |
| title | Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology |
| title_full | Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology |
| title_fullStr | Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology |
| title_full_unstemmed | Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology |
| title_short | Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology |
| title_sort | application of artificial intelligence in the diagnosis of malignant digestive tract tumors focusing on opportunities and challenges in endoscopy and pathology |
| topic | Malignant digestive tract tumors Endoscopy Pathology Artificial intelligence Deep learning Diagnosis |
| url | https://doi.org/10.1186/s12967-025-06428-z |
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