Harnessing artificial intelligence in gastroenterology and hepatology: Current applications and future perspectives

Table 1 Comparison of key artificial intelligence approaches relevant to gastroenterology and hepatology

AI technique	Input data type	Interpretability	Computational cost	Common clinical applications	Limitations
Logistic regression	Structured tabular data	High	Low	Risk scoring (e.g., cirrhosis, NAFLD), binary classification	Assumes linear relationships, limited complexity handling
Decision trees/random forests	Structured data, some semi-structured	Moderate to high	Moderate	Prognostic models (e.g., HCC recurrence), treatment stratification	Can overfit, less effective on unstructured data
SVM	Structured data, imaging (preprocessed)	Low to moderate	Moderate	Classification tasks (e.g., benign vs malignant lesions)	Less scalable, needs careful kernel tuning
CNNs	Imaging data (e.g., endoscopy, CT, MRI)	Low	High	Polyp detection, liver lesion classification, fibrosis staging	Black-box nature, high data requirements
RNNs/LSTMs	Time-series data, text sequences	Low	High	Monitoring biomarkers over time, EHR text analysis	Difficult to train, prone to vanishing gradients
Transformers/LLMs	Natural language, multimodal inputs	Moderate to low	Very high	Summarization of clinical notes, patient stratification via EHR	Expensive to fine-tune, interpretability challenges
Autoencoders/unsupervised learning	Imaging, high-dimensional data	Low	Moderate to high	Anomaly detection, feature extraction	Requires careful architecture design, lacks direct supervision
Federated learning	Decentralized structured/unstructured data	Moderate	High	Multi-center model training without data sharing	Complex orchestration, risk of data heterogeneity bias

AI: Artificial intelligence; SVM: Support vector machines; CNNs: Convolutional neural networks; RNNs: Recurrent neural networks; LLMs: Large language models; NAFLD: Non-alcoholic fatty liver disease; HCC: Hepatocellular carcinoma; CT: Computed tomography; MRI: Magnetic resonance imaging; LSTMs: Long short-term memory networks; EHR: Electronic health record.