Decision-Making in Severe Acute Pancreatitis: The role of Artificial Intelligence and Severity Scales

Abstract

Severe acute pancreatitis (SAP) presents a complex clinical scenario that demands prompt and accurate decision-making regarding the appropriate course of treatment. The management of SAP involves a delicate balance between surgical intervention and conservative therapy, aiming to optimize patient outcomes while minimizing morbidity and mortality. Traditional methods of assessing disease severity, such as the Balthazar scale, Ranson criteria, Glasgow-Imrie score, and APACHE II score, provide valuable clinical insight but may lack the precision necessary for individualized patient care. In recent years, integrating artificial intelligence (AI) technologies into healthcare has shown promise in augmenting clinical decision-making processes. By leveraging machine learning algorithms and predictive analytics, AI has the potential to enhance the accuracy and efficiency of severity assessment in SAP. This article explores the role of AI in conjunction with existing severity scales in aiding surgeons' decision-making regarding the timing and modality of intervention in patients with SAP. Through a comprehensive review of current literature and case studies, we will examine the advantages and limitations of AI-based approaches and propose strategies for integrating these technologies into clinical practice. By harnessing the power of AI, surgeons can potentially optimize patient outcomes, improve resource utilization, and reduce the burden of SAP on healthcare systems worldwide.