Artificial Intelligence in Pancreatic Resection: Current Evidence, Surgical Applications, and a Framework for Clinical Translation
- 1Department of General Surgery, Royal Blackburn Teaching Hospital, East Lancashire Hospitals NHS Trust, Haslingden Road, Blackburn BB2 3HH, UK
Abstract
Introduction: Pancreatic resection remains technically demanding, with clinically relevant postoperative pancreatic fistula (CR-POPF) a major source of morbidity. Artificial intelligence (AI) may support risk stratification, operative planning, intraoperative assessment, and postoperative decision support. This focused narrative review evaluates current AI applications in pancreatic resection and their translational maturity. Methods: A structured PubMed/MEDLINE search with an evidence cutoff of 24 July 2026 was supplemented by backward and forward citation tracking. Seventy eligible primary reports were synthesised by clinical task, information timing, and validation maturity. Results: Evidence is dominated by CR-POPF prediction after pancreatoduodenectomy, with additional studies in distal pancreatectomy, other perioperative outcomes, tissue and margin assessment, three-dimensional anatomical planning, and intraoperative video and anatomy recognition. Performance varied with patient selection, predictor timing, data modality, and validation strategy. External validation often changed reported discrimination, while calibration and clinical utility were inconsistently reported. Most studies were retrospective or technical; overlapping cohorts limited independence, and no included study demonstrated improved patient-centred outcomes. Conclusions: AI in pancreatic resection is expanding across prediction, planning, and guidance, but technical performance should not be equated with clinical readiness. Independent validation, prospective workflow and human-factors evaluation, and clinical-impact studies are needed. AI should augment, rather than replace, specialist surgical judgement.
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BibTeX
@article{heiba2026artificial,
title = {{Artificial Intelligence in Pancreatic Resection: Current Evidence, Surgical Applications, and a Framework for Clinical Translation}},
author = {Karim Heiba and Ambareen Kausar},
journal = {Journal of Clinical Medicine},
year = {2026},
month = nov,
doi = {10.3390/jcm15218737},
url = {https://www.mdpi.com/2077-0383/15/21/8737},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Pancreatic Resection: Current Evidence, Surgical Applications, and a Framework for Clinical Translation
AU - Karim Heiba
AU - Ambareen Kausar
PY - 2026
DA - 2026/11/09
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15218737
UR - https://www.mdpi.com/2077-0383/15/21/8737
AB - Introduction: Pancreatic resection remains technically demanding, with clinically relevant postoperative pancreatic fistula (CR-POPF) a major source of morbidity. Artificial intelligence (AI) may support risk stratification, operative planning, intraoperative assessment, and postoperative decision support. This focused narrative review evaluates current AI applications in pancreatic resection and their translational maturity. Methods: A structured PubMed/MEDLINE search with an evidence cutoff of 24 July 2026 was supplemented by backward and forward citation tracking. Seventy eligible primary reports were synthesised by clinical task, information timing, and validation maturity. Results: Evidence is dominated by CR-POPF prediction after pancreatoduodenectomy, with additional studies in distal pancreatectomy, other perioperative outcomes, tissue and margin assessment, three-dimensional anatomical planning, and intraoperative video and anatomy recognition. Performance varied with patient selection, predictor timing, data modality, and validation strategy. External validation often changed reported discrimination, while calibration and clinical utility were inconsistently reported. Most studies were retrospective or technical; overlapping cohorts limited independence, and no included study demonstrated improved patient-centred outcomes. Conclusions: AI in pancreatic resection is expanding across prediction, planning, and guidance, but technical performance should not be equated with clinical readiness. Independent validation, prospective workflow and human-factors evaluation, and clinical-impact studies are needed. AI should augment, rather than replace, specialist surgical judgement.
SN - 2077-0383
ER -