Artificial intelligence in acute appendicitis: A comprehensive review of machine learning and deep learning applications

Table 2 Definitions of artificial intelligence techniques employed in acute appendicitis research

Method	Definition	Relation to deep learning	Advantages
Deep learning	A subset of ML that uses multi-layered neural networks to automatically extract features from large datasets	DL is commonly used in image analysis text processing and predictive modeling. FL and edge AI can enhance the efficiency and privacy of DL models	High ACC strong capability in handling image and language data
Federated learning	A decentralized ML approach where models are trained across multiple institutions without sharing patient data	FL allows DL models to be trained across different centers while preserving patient privacy. It is useful for multi-center AI studies in appendicitis diagnosis	Enhances data privacy allows for cross-institutional AI model development
Edge AI	AI models that run directly on local hospital devices portable ultrasound scanners or mobile systems instead of relying on cloud computing	Edge AI enables DL models to operate in real-time on local devices reducing dependence on internet connectivity	Real-time processing improved data security reduced latency in decision-making
Bayesian networks	Probabilistic models that establish relationships between variables and handle uncertainty in data	Can be integrated with DL models to improve decision-making under incomplete information	Useful for risk prediction particularly in cases with missing clinical data
Transformer-based AI models (BERT, GPT)	Large language models capable of understanding and processing medical text	Can be used in combination with DL for automated triage systems and clinical note analysis	Efficient text processing potential for real-time clinical decision support
Graph neural networks	AI models that analyze relationships between data points in a structured graph format	GNNs can enhance DL models by incorporating complex patient relationships and comorbidities	Improves risk prediction models enhances interpretability of patient data interactions
Automated machine learning	AI systems that automatically optimize model selection hyperparameters and feature engineering	AutoML can generate optimized DL models without requiring manual tuning	Reduces the need for expert AI developers accelerates model deployment
Natural language processing	AI systems designed to interpret and extract information from human language including clinical notes and radiology reports	NLP models can be integrated with DL to analyze unstructured medical data	Enhances electronic health record analysis supports AI-assisted triage systems
Computer vision	AI field enabling machines to interpret visual data particularly useful in medical imaging	Computer vision models. including DL-based CNNs improve diagnostic ACC in radiology	Reduces diagnostic variability. increases ACC in CT and MRI interpretation
Reinforcement learning and explainable AI	AI models that learn optimal decision pathways based on cumulative rewards XAI ensures transparency in model predictions	Can optimize treatment strategies while SHAP and LIME techniques make AI models interpretable for clinicians	Improves AI adoption in healthcare enables better treatment planning
Machine learning	A broad AI field encompassing various algorithms including supervised and unsupervised learning	ML models, such as SVM, random forest and XGBoost form the foundation for AI in clinical decision-making	Provides adaptable and scalable models for medical data analysis
Vision transformers	A deep learning model specifically designed for image segmentation and classification	Enhances medical image analysis by capturing spatial relationships within radiology images	Improves segmentation ACC particularly in CT and MRI-based diagnosis
Lazy learning algorithms (KNN)	Classification method that identifies the closest data points in a dataset	Used in ML for patient clustering and classification	Simple yet effective but computationally expensive in large datasets
Extra trees classifier	A variant of random forest that introduces additional randomness to improve ACC	Works alongside ensemble learning to enhance classification performance	High ACC robustness in medical data analysis
Hybrid AI models	AI models combining ML and DL techniques to improve diagnostic performance	Used in multimodal AI-based appendicitis detection	Enhances ACC by integrating structured and unstructured data sources

AI: Artificial intelligence; BERT: Bidirectional encoder representations from transformers; GPT: Generative pre-trained transformer; KNN: K-nearest neighbor; ML: Machine learning; XAI: Explainable artificial intelligence; ACC: Accuracy; DL: Deep learning; FL: Federated learning; GNN: Graph neural network; AutoML: Automated machine learning; NLP: Natural language processing; CNN: Convolutional neural network; SHAP: SHapley Additive exPlanations; LIME: Local interpretable model-agnostic explanations; SVM: Support vector machine; XGBoost: Extreme gradient boosting; CT: Computed tomography; MRI: Magnetic resonance imaging.