A chronological overview of AI applications in colorectal cancer diagnosis, prognosis, and treatment.
| Citation | Authors (year) | AI model | Advantages | Disadvantages |
|---|---|---|---|---|
| [259] | Ferrari et al. (2019) | MR-based AI model | Predicts therapy response, non-invasive | Requires high-quality MRI, expensive technology |
| [282] | Yang et al. (2019) | Deep learning for imaging | High accuracy, non-invasive diagnosis | Requires large datasets, high computational resources |
| [283] | Mao et al. (2020) | MRI | High specificity for liver metastasis, non-invasive | High costs, availability limitations |
| [234] | Zeng et al. (2020) | PR-OCT with deep learning | Rapid diagnosis, high sensitivity | Expensive technology, requires specialized equipment |
| [239] | Wang et al. (2020) | AI for diagnosis and therapy | Improves diagnostic accuracy, potential for therapy optimization | High initial costs, need for integration into clinical practice |
| [248] | Kudo et al. (2020) | AI-assisted endoscopy | Improves adenoma detection rates, real-time analysis | Potential over-reliance on AI, need for high-quality images |
| [249] | Lui et al. (2020) | AI in colonoscopy | Reduces missed polyps, enhances detection rates | False positives, potential for over-screening |
| [250] | Sinagra et al. (2020) | AI for adenoma detection | Enhances detection rates, supports endoscopists | Requires large datasets for training, potential biases in AI models |
| [251] | Zhou et al. (2020) | Deep learning for optical diagnosis | High accuracy in optical diagnosis, non-invasive | Requires large datasets, expensive to implement |
| [225] | News-Medical.Net (2021) | AI-driven imaging | Real-time analysis, improved detection rates | Limited by data quality, potential biases |
| [253] | Hsiao et al. (2021) | AI in endoscopic screening | Early detection, high sensitivity | Expensive technology, requires specialized equipment |
| [284] | Parsa et al. (2021) | AI for polyp characterization | Reduces human error, enhances polyp characterization | Potential for over-reliance on AI, requires continuous updates |
| [285] | Wang et al. (2021) | AI for polyp detection | Improves detection rates, real-time classification | Requires large datasets, potential for false positives |
| [260] | Wang et al. (2021) | MRI-based AI model | High specificity for rectal cancer, non-invasive | High costs, limited availability |
| [266] | Sirinukunwattana et al. (2021) | Deep learning for subtyping | High accuracy in molecular subtyping, aids in personalized treatment | Requires large datasets, expensive to implement |
| [267] | Wang et al. (2021) | AI for histopathology | High accuracy in diagnosis, supports pathologists | Requires large annotated datasets, potential biases in AI models |
| [268] | Yu et al. (2021) | Semi-supervised deep learning | Reduces need for labeled data, high accuracy | Computationally intensive, requires continuous updates |
| [235] | Bilal et al. (2023) | Digital pathology with AI | Facilitates large-scale analysis, enhances pathology workflow | Potential for over-reliance on AI, data security concerns |
| [236] | Yu et al. (2022) | ML | Predictive capabilities, personalized treatment plans | Algorithm complexity, requires ongoing updates |
| [252] | Barua et al. (2022) | AI-based optical diagnosis | Real-time analysis, high accuracy in polyp detection | Potential for false positives, requires high-quality images |
| [254] | Messmann et al. (2022) | AI in gastrointestinal endoscopy | Standardizes detection, improves consistency | High costs, requires extensive training |
| [255] | Wallace et al. (2022) | AI in neoplasia detection | Reduces miss rates, enhances detection accuracy | Potential over-reliance on AI, needs constant updates |
| [261] | Villamanca et al. (2022) | AI with Fourier transform infrared | Non-invasive, high sensitivity | Requires specialized equipment, limited clinical application |
| [263] | Waljee et al. (2022) | AI/ML for early detection | Early detection in low-resource settings, scalable | Requires data and infrastructure, potential biases in training data |
| [269] | Ho et al. (2022) | Deep learning for histopathology | High accuracy, supports pathologists | Requires high-quality images, potential for false positives |
| [270] | Ju et al. (2022) | AI for pathological staging | High accuracy, non-invasive staging | Requires large datasets, expensive to implement |
| [256] | Koh et al. (2023) | AI-aided endoscopy | Enhances detection rates, supports experienced endoscopists | Expensive, requires integration into clinical practice |
| [257] | Spadaccini et al. (2023) | AI-aided endoscopy | Improves screening accuracy, real-time feedback | Potential for false positives, requires large training datasets |
| [262] | Gerwert et al. (2023) | AI-integrated infrared imaging | Label-free detection, fast results | Expensive technology, requires specialized training |
| [264] | Ziegelmayer et al. (2023) | Deep learning for CT imaging | Differentiates conditions accurately, non-invasive | Requires high-quality CT images, potential for misclassification |
| [273] | Bilal et al. (2023) | AI-based prescreening | Reduces workload for pathologists, improves efficiency | Potential for over-reliance on AI, data security concerns |
| [274] | Griem et al. (2023) | AI for tumor detection | Enhances detection accuracy, supports tissue analysis | Requires high-quality images, potential for false positives |
| [275] | Prezja et al. (2023) | Refined deep learning | High accuracy, supports tissue decomposition analysis | Requires large datasets, high computational resources |
| [272] | Saillard et al. (2023) | AI for MSI detection | High accuracy, supports pre-screening | Expensive to implement, requires specialized training |
| [120] | Wagner et al. (2023) | Transformer-based AI | High accuracy, supports biomarker prediction | Requires large datasets, computationally intensive |
| [191] | Yin et al. (2023) | Deep learning | High accuracy in diagnosis, early detection | Requires large datasets for training, high computational resources |
| [286] | Yin et al. (2023) | Generalized AI with transfer learning | High flexibility, supports multiple tasks | Requires large datasets, potential for overfitting |
| [279] | Pham et al. (2023) | AI fusion | Combines multiple data sources, high accuracy | Requires high-quality data, expensive to implement |
| [280] | Jiang et al. (2023) | Deep learning for MRI | Predicts outcomes, supports clinical decisions | Requires high-quality MRI, computationally expensive |
| [281] | L’Imperio et al. (2023) | ML for risk stratification | High accuracy, supports clinical risk assessment | Requires validation, potential for misclassification |
| [287] | Pham et al. (2023) | Markov models with AI | Predicts survival outcomes, supports clinical decision-making | Requires large datasets, computationally intensive |
| [288] | Tsai et al. (2023) | AI for multi-omics prediction | High accuracy, supports personalized medicine | Requires high-quality data, expensive to implement |
| [258] | Spadaccini et al. (2024) | AI-assisted colonoscopy | Enhances screening, reduces miss rates | Expensive, requires integration into existing systems |
| [265] | Peng et al. (2024) | ML for image fusion | Combines multiple imaging modalities, improves accuracy | Computationally expensive, requires large datasets |
| [271] | Neto et al. (2024) | Interpretable ML system | High interpretability, supports diagnosis | Requires high-quality images, potential for misclassification |
AI: artificial intelligence; MRI: magnetic resonance imaging; CT: computed tomography; ML: machine learning.
AI-Assisted Work Statement: During the preparation of this work, the authors used MS PowerPoint and ChatGPT to put together the text and icons to generate the graphical abstract. After using MS PowerPoint and ChatGPT to generate the image, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
TDP: Conceptualization. DTPL: Writing—original draft, Conceptualization. Both authors read, edited, and approved the submitted version.
Dr. Tuan D. Pham, who is the Editorial Board Member of Exploration of Medicine, had no involvement in the decision-making or review process of this manuscript. The other author declares that he has no conflicts of interest.
Not applicable.
Not applicable.
Not applicable.
Not applicable.
Not applicable.
© The Author(s) 2025.
Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.