Explainable artificial intelligence for personalized management of inflammatory bowel disease: A minireview of recent advances

Table 1 Overview of studies selected for the review of explainable artificial intelligence techniques in inflammatory bowel disease care

Ref.	XAI technique	IBD type	Modality	Classifier	Contribution	Evaluation metrics (highest)
Onwuka et al[14]	SHAP	CD and UC	Multiomics dataset	LGBM	This study applies SHAP-based XAI to identify key fecal metabolites that robustly predict IBD and their diet associations. It demonstrates that XAI not only enhances metabolite prioritization across datasets but also clarifies how diet-driven microbial metabolites distinctly influence IBD pathology	AUC = 0.93 (distinguishing between IBD and non-IBD cases)
Patel et al[26]	Grad-CAM, saliency maps, integrated gradients, and LIME	UC	Endoscopic images	ResNet50 and MobileNetV2	This study evaluates four XAI techniques predictions in IBD-related endoscopic image classification. The analysis reveals that Grad-CAM most consistently highlights relevant regions, supporting its use in visualizing model focus and validating TeleXGI’s multi-XAI strategy for clinical interpretability	ACC = 98.8% (health risk prediction of UC endoscopic images remotely)
Chierici et al[37]	Saliency and gradient-based techniques	CD and UC	Endoscopic images	DenseNet121	This study applied saliency and guided backpropagation to a ResNet50 model for IBD endoscopic image classification. The attribution maps revealed clinically relevant features, with guided backpropagation offering clearer, less noisy insights	MCC = 0.94 (classification of healthy controls and IBD patients)
Sutton et al[38]	Grad-CAM	UC	Endoscopic images	DenseNet121	This study uses explainable deep learning on HyperKvasir endoscopic images to accurately distinguished UC from non-UC conditions and stratified disease severity based on Mayo scores. The DenseNet121 model achieved the highest performance, with Grad-CAM enhancing interpretability through visual explanations	AUC = 0.90 (classification of UC and non-UC images)
Tsai and Lee[39]	Grad-CAM	UC	Endoscopic images	DenseNet201, InceptionV3, and VGG19	This study leverages Grad-CAM to interpret deep learning model predictions for UC classification, comparing individual CNNs and ensemble models. The triplet ensemble produced the most focused and clinically relevant heatmaps	ACC = 91%
Ma et al[40]	Grad-CAM	CD	Ultrasound images and clinical data	ResNet50	This study developed a deep learning model combining intestinal ultrasound images and clinical data to predict mucosal healing in CD after one year of treatment. Using Grad-CAM for interpretability, the model highlighted key features such as the bowel wall and mesentery, achieving a high PPV	AUC = 0.73 (classification of mucosal healing and non-mucosal healing)
Maurício and Domingues[41]	Grad-CAM, LIME, SHAP, occlusion sensitivity	CD and UC	Endoscopic images	CNN + LSTM	This study develops CNN and ViT models to classify CD and UC from endoscopic images, creating lighter versions via knowledge distillation for clinical use. The ViT-S/16 model achieved the best performance, accurately identifying IBD features while ignoring irrelevant elements. Multiple XAI interpretability analysis confirmed its reliability, with minimal misclassifications after distillation using temperature-based methods	ACC = 95% (distinguishing between active and non-active inflammation)
Weng et al[42]	SHAP and LIME	CD	Clinical data	Extreme gradient boosting	This study integrates SHAP and LIME to interpret an extreme gradient boosting-based model for differentiating intestinal tuberculosis from CD. These XAI methods successfully identified and visualized the key clinical features influencing the machine learning model’s predictions for differentiating intestinal tuberculosis and CD	MCC = 96.9%
Zhen et al[43]	LIME	CD and UC	Clinical data	SVC	This study applied LIME to interpret an SVM model predicting quality-of-life impairment in IBD patients, identifying key modifiable risk factors such as anxiety, abdominal pain, and glucocorticoid use	AUC = 80% (evaluating IBD-related quality-of-life impairments)
Deng et al[44]	Trainable attention mechanisms	CD	Pathological images	RFC and GNN	This study introduces a cross-scale attention mechanism for multi-instance learning that captures inter-scale interactions in whole slide images for CD diagnosis. Trained on approximately 250000 hematoxylin and eosin-stained patches. Cross-scale attention visualizations localize lesion patterns at different magnifications, enhancing both diagnostic accuracy and model interpretability	AUC = 0.89 (distinguishing between healthy CD patient)
de Maissin et al[45]	Trainable attention mechanisms	CD	Endoscopic images	ResNet 34	This study introduces a recurrent attention neural network trained on a multi-expert annotated CD capsule endoscopy dataset. It demonstrates that higher annotation quality significantly boosts diagnostic accuracy (up to 93.7% precision). The network mimics human visual focus, enabling interpretable lesion localization and outperforming standard CNNs as annotations improve	ACC = 94.6% (detection of pathological and non-pathological images)
Wu et al[46]	Trainable attention mechanisms	UC	Endoscopic images	FLATer	This study presents FLATer, an explainable transformer-based model combining CNN and ViT for GIT disease classification. An ablation study reveals the crucial role of the residual block and spatial attention in boosting performance and interpretability, with saliency maps confirming enhanced localization of pathological region	ACC = 99.7% (multi-class classification to categorize images into specific diseases)
Sucipto et al[47]	Trainable attention mechanisms	CD	Pathological images	RFC and GNN	This study introduces a cross-scale attention mechanism for multi-instance learning that captures inter-scale interactions in whole slide images for CD diagnosis. Trained on approximately 250000 hematoxylin and eosin-stained patches, it achieved an AUC of 0.8924. Cross-scale attention visualizations localize lesion patterns at different magnifications, enhancing both diagnostic accuracy and model interpretability	ACC = 87% and 85% (prediction of histologic remission)
Ahamed et al[48]	SHAP, heatmap, Grad-CAM, and saliency maps	UC	Endoscopic images	EELM	This study introduces a lightweight, parallel-depth CNN optimized for gastrointestinal image classification using the GastroVision dataset, including IBD cases. Integrated with multiple XAI techniques (Grad-CAM, SHAP, saliency maps), the model enhances interpretability and diagnostic transparency. Using EELM for final classification, the system achieved high accuracy, robust generalizability	AUC = 0.987 (distinguishing between normal and benign ulcer)
Elmagzoub et al[49]	Trainable attention mechanisms	UC	Endoscopic images	ResNet101	This study presents a grid search-optimized ResNet101 model with an integrated attention mechanism for classifying gastrointestinal diseases, including UC, from endoscopic images	ACC = 93.5%

IBD: Inflammatory bowel diseases; XAI: Explainable artificial intelligence; SHAP: SHapley Additive exPlanations; Grad-CAM: Gradient-weighted class activation mapping; LIME: Local interpretable model-agnostic explanation; UC: Ulcerative colitis; CD: Crohn’s disease; EELM: Ensemble extreme learning machine; RFC: Random forest classifier; GNN: Graph neural network; SVC: Support vector classifier; CNN: Convolutional neural network; LSTM: Long short-term memory; LGBM: Light gradient boosting machine; ViT: Vision transformer; EBV: Epstein-Barr virus; SVM: Support vector machine; GIT: Gastrointestinal tract; AUC: Area under the curve; ACC: Accuracy; MCC: Matthews correlation coefficient.