From prediction to clinical decision-making: Explainable machine learning in acute suppurative cholecystitis

Table 1 Summary of recent studies on machine learning and radiomics in acute biliary disease and surgical risk prediction

Ref.	Year	Population	Modality	Model	Key findings	Translational value	Limitations	Gap identified
Demircioğlu[31]	2025	Radiomics methods	Imaging	ML frameworks	Reproducibility critical	High	Pipeline variability	No clinical translation
Ma et al[32]	2024	Acute biliary disease	CT radiomics	XGBoost + SHAP	Interpretable early severity prediction	High	Single-center	Lack of validation in cholecystitis-specific cohorts
Kenig et al[38]	2024	Surgical AI	Mixed	ML models	Improved preoperative prediction	Moderate	Limited validation	Lack of explainability integration
Cè et al[37]	2024	Radiomics workflow	CT/MRI	ML pipelines	Importance of pipeline standardization	High	Variability	No direct clinical application
Teng et al[36]	2024	Radiomics	Imaging	ML models	Focus on clinical integration challenges	High	Limited deployment	Lack of real-time decision tools
Sadeghi et al[40]	2024	Healthcare AI	Mixed	XAI models	Explainability improves trust	High	Misinterpretation risk	Limited disease-specific application
Borys et al[41]	2023	Imaging AI	Imaging	SHAP/XAI	Enables patient-level interpretation	High	Needs validation	Not integrated in surgical workflows
Erickson et al[9]	2019	Imaging review	Multimodal	ML overview	Highlights ML potential	Low-Moderate	Conceptual only	No clinical implementation
Bi et al[10]	2019	Imaging datasets	CT radiomics	ML classifiers	Quantifies imaging heterogeneity	Moderate	Feature instability	No integration with clinical variables
Hosny et al[8]	2018	Imaging datasets	CT radiomics	Deep learning	Demonstrates scalability	Moderate	Generalizability issues	Not tailored to acute surgical decision-making

SHAP: SHapley Additive exPlanations; CT: Computed tomography; ML: Machine learning; MRI: Magnetic resonance imaging; XAI: Explainable Artificial Intelligence; AI: Artificial intelligence; XGBoost: Extreme Gradient Boosting.