Endoscopic image analysis assisted by machine learning: Algorithmic advancements and clinical uses

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Artificial Intelligence in Gastrointestinal Endoscopy

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2689-7164

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Artif Intell Gastrointest Endosc. Sep 8, 2025; 6(3): 108281
Published online Sep 8, 2025. doi: 10.37126/aige.v6.i3.108281

Table 1 Comparison of two machine learning models

	Supervised model	Unsupervised model
Date dependency	High-quality annotations	Label-efficient
Clinical applicability	Task-specific optimization	Exploratory applications
Interpretability	Moderate (via feature mapping)	Low (abstract feature hierarchy)
Deployment	Device-specific optimization	Generalizable frameworks

Citation: Ding JC, Zhang J. Endoscopic image analysis assisted by machine learning: Algorithmic advancements and clinical uses. Artif Intell Gastrointest Endosc 2025; 6(3): 108281
URL: https://www.wjgnet.com/2689-7164/full/v6/i3/108281.htm
DOI: https://dx.doi.org/10.37126/aige.v6.i3.108281

Ding JC, Zhang J. Endoscopic image analysis assisted by machine learning: Algorithmic advancements and clinical uses. Artif Intell Gastrointest Endosc 2025; 6(3): 108281 [DOI: 10.37126/aige.v6.i3.108281]

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