Copyright: ©Author(s) 2026.
Artif Intell Gastroenterol. Nov 8, 2026; 7(3): 125126
Published online Nov 8, 2026. doi: 10.35712/aig.125126
Published online Nov 8, 2026. doi: 10.35712/aig.125126
Table 2 Artificial intelligence applications across perioperative phases and nursing implications in gastrointestinal and complex surgical care
| Perioperative phase | AI-supported function | Surgical context | Nursing implications | Measurable nursing-sensitive indicators |
| Preoperative assessment | Risk stratification using demographic, laboratory, imaging, nutritional, and comorbidity data[5,25-30] | Pancreaticoduodenectomy; major hepatectomy; colorectal cancer surgery; multivisceral resection | Identification of patients requiring intensified education, nutritional support, prehabilitation, early stoma counselling, or planned higher-acuity postoperative monitoring | Completion of risk-based education; timely nutrition or physiotherapy referral; documentation of individualized monitoring plan; predefined escalation criteria |
| Preoperative imaging review | AI-assisted detection of sarcopenia, visceral obesity, frailty-related imaging markers, or anatomical complexity[34,35] | Hepatopancreatobiliary and oncological gastrointestinal surgery | Translation of imaging-derived risk into practical nursing care, including pressure-injury prevention, mobilization planning, respiratory preparation, and discharge support | Documentation of frailty or nutritional risk; early mobilization plan; referral to allied health professionals; pressure-injury risk reassessment |
| Operating room and immediate recovery | Prediction of hemodynamic instability, bleeding risk, respiratory compromise, or need for higher-acuity care[25,30,35] | Esophagectomy; major liver resection; complex abdominal surgery | Structured handover from operating room or recovery unit to ward or intensive care, with clear monitoring priorities | Completeness of AI-informed handover; time from recovery discharge to first ward reassessment; documentation of key intraoperative concerns |
| Early postoperative period | Detection of abnormal physiological patterns and early deterioration[5-9,25,30] | Anastomotic leak after colorectal surgery; pancreatic fistula; postoperative bleeding; sepsis; acute kidney injury | Prompt bedside reassessment, review of drain output, wound status, fluid balance, pain pattern, urine output, oxygen requirement, and escalation to surgical team | Time from alert to nursing reassessment; time from reassessment to medical escalation; proportion of alerts with documented response; missed or delayed escalation events |
| Postoperative ward care | Support for risk-stratified enhanced recovery pathways[10-13,47-49] | Colorectal surgery; pancreatic surgery; hepatobiliary surgery | Adjustment of monitoring frequency, mobilization targets, pain assessment, nutritional support, and discharge readiness according to risk | Adherence to individualized observation schedule; completion of ERAS-related nursing interventions; documented reasons for pathway deviation |
| Discharge planning | Prediction of readmission, delayed recovery, dehydration, infection, or poor self-management[5,25,27,30] | New ileostomy; complex wounds; drains at discharge; frail older patients; cancer surgery | Targeted patient education, confirmation of understanding, community nursing referral, stoma nurse involvement, and early follow-up planning | Completeness of discharge education; documentation of warning signs; referral to community or stoma services; early post-discharge contact; avoidable readmission |
- Citation: Kapritsou M. Nursing leadership and performance metrics in artificial intelligence integrated gastrointestinal surgical care: A narrative review. Artif Intell Gastroenterol 2026; 7(3): 125126
- URL: https://www.wjgnet.com/2644-3236/full/v7/i3/125126.htm
- DOI: https://dx.doi.org/10.35712/aig.125126