Nursing leadership and performance metrics in artificial intelligence integrated gastrointestinal surgical care: A narrative review

Table 1 Evidence synthesis of selected artificial intelligence studies relevant to gastrointestinal surgical nursing

Ref.	Setting/population	AI task	Validation and model performance	Implementation/clinical effectiveness	Applicability to nursing	Evidence relationship
Wang et al[5]	Systematic review and meta-analysis; 38 studies of gastrointestinal surgery	Prediction of postoperative outcomes	143 ML and 62 logistic-regression models; ML showed higher discrimination than LR (ΔAUC 0.07, 95%CI: 0.04-0.09); 44% of studies were at low risk of bias. Calibration and external validation were inconsistently reported across the underlying literature	Practical implementation and patient-level clinical effectiveness were not established	Relevant to GI surgical risk stratification and surveillance, but nursing-specific implementation and outcomes were not evaluated	Indirect
Bihorac et al[25]	Development/validation study; 51457 adults undergoing major inpatient surgery at a single center	Prediction of eight major postoperative complications and mortality	Repeated internal validation; complication AUCs 0.82-0.94 and mortality AUCs 0.77-0.83. Calibration was assessed. No external validation in this study	Designed for real-time perioperative use, but feasibility in clinical workflow required further prospective testing; clinical effectiveness was not demonstrated in this study	Potentially relevant to risk-based monitoring and escalation, but not GI-specific and nursing outcomes were not evaluated	Indirect
Bektaş et al[27]	Systematic review; 31 studies of colorectal surgery	Prediction of surgical outcomes using ML	Most studies were retrospective; external validation occurred in 4 studies (13%) and calibration was reported for 3 models (10%)	Routine clinical implementation and improvement in patient outcomes were not established	Directly relevant to colorectal surgical risk prediction; applicability to nursing workflow remains indirect because nursing-specific outcomes were not studied	Indirect
Bektaş et al[29]	Systematic review; 52 studies of hepatopancreaticobiliary surgery	Prediction of disease course, postoperative complications and other surgical outcomes	47/52 (90%) studies were retrospective and 5/52 (10%) prospective; external validation was generally absent	Authors identified external validation as necessary before clinical introduction; routine clinical effectiveness was not established	Relevant to procedure-specific risk assessment, but nursing implementation and nursing-sensitive outcomes were not evaluated	Indirect
Soria-Utrilla et al[34]	Retrospective observational study; 586 patients with colorectal cancer assessed before surgery	AI-assisted CT body-composition assessment	SMI discriminated low FFMI with AUC 0.82; sex-specific diagnostic cut-offs were derived within the study population. External validation was not reported	Diagnostic/morphofunctional assessment was evaluated; effects on downstream workflow or patient outcomes were not tested	May identify nutritional and functional vulnerability relevant to preoperative assessment and referral; direct nursing effects were not evaluated	Indirect
Cho et al[70]	Multihospital implementation study; 4 hospitals with different electronic record systems	One-hour fall-risk prediction with AI-powered decision support and tailored nursing recommendations	Locally optimized models were implemented across four sites; AUROC 0.8051-0.9581	Successfully integrated into different EMR systems. Nursing interventions increased, but fall rates did not significantly decrease	Provides direct evidence that AI can be incorporated into nursing workflow and outcomes tracking, but the setting was not gastrointestinal surgery	Direct nursing implementation evidence; non-GI setting

AI: Artificial intelligence; AUC: Area under the curve; AUROC: Area under the receiver operating characteristic curve; CT: Computed tomography; EMR: Electronic medical record; FFMI: Fat-free mass index; GI: Gastrointestinal; LR: Logistic regression; ML: Machine learning; SMI: Skeletal muscle index.