Role of Artificial Intelligence in Pancreatic Cyst Diagnosis

Pancreatic cystic lesions have become increasingly prevalent and diagnostically challenging due to their diverse characteristics and potential for malignant transformation in certain cases. Accurate risk stratification is crucial for optimizing surveillance and intervention strategies. However, conventional diagnostic modalities, such as radiologic interpretation, endoscopic assessment, and cyst fluid analysis, often yield ambiguous or discordant findings. Artificial Intelligence (AI) has emerged as a key innovation capable of addressing these limitations through advanced computational analysis of complex, high-dimensional data. Contemporary AI models, including deep convolutional neural networks and multimodal machine learning architectures, demonstrate significant improvements in cyst detection, automated segmentation, and ability to differentiate between nonmucinous and mucinous lesions across computed tomography (CT), magnetic resonance imaging (MRI), and endoscopic ultrasound (EUS) imaging. Radiomic and pathologic feature extraction, when integrated with clinical, biochemical, and molecular signatures, further enhances predictive accuracy for dysplasia grading and malignancy risk assessment. Notably, AI-enabled decision support systems have the potential to reduce observer variability, refine guideline-based management algorithms, and minimize unnecessary surgical resections. However, the translation of AI into routine clinical practice is constrained by limitations in dataset heterogeneity, reproducibility, external validation, and algorithm interpretability. Ethical concerns—including data governance, model transparency, and potential biases—emphasize the necessity of rigorous methodological standards and regulatory oversight. This chapter synthesizes extant evidence, evaluates emerging AI methodologies, and outlines critical challenges and future directions. Collectively, AI is poised to transform the diagnostic paradigm for pancreatic cysts, advancing precision medicine and enhancing patient outcomes through more reliable, data-driven risk assessment.