Machine Learning for Risk Stratification in Acute Pancreatitis: Current Evidence and Future Perspectives

Area of Science:

• Gastroenterology and Hepatology
• Medical Informatics
• Artificial Intelligence in Medicine

Background:

• Acute pancreatitis (AP) presents a variable clinical course, necessitating early risk stratification for optimal patient outcomes.
• Conventional prognostic scoring systems for AP have limitations in accuracy, applicability, and data requirements.
• Advances in AI and ML offer potential for more accurate early prediction of AP severity and complications.

Purpose of the Study:

• To review the current evidence on AI and ML applications in predicting AP outcomes.
• To focus on AI/ML for predicting disease severity, organ failure, ICU admission, and mortality in AP.
• To discuss explainable AI, model limitations, and future directions for AI integration in AP management.

Main Methods:

• Literature review of studies applying AI and ML to acute pancreatitis.
• Analysis of models integrating demographic, clinical, laboratory, and imaging data.
• Evaluation of AI/ML approaches for predicting AP severity, organ failure, ICU admission, and mortality.

Main Results:

• AI and ML models demonstrate potential for improved early prediction of AP severity and patient outcomes.
• These models can integrate diverse data types for enhanced predictive performance compared to traditional methods.
• Current research highlights the need for robust validation, calibration, and reproducibility of AI/ML models in AP.

Conclusions:

• AI and ML hold significant promise for improving early risk stratification and outcome prediction in acute pancreatitis.
• Addressing methodological limitations and ensuring prospective validation are crucial for clinical integration.
• Future research should focus on multimodal AI and regulatory frameworks for safe implementation of AI-based decision support in AP.