Keywords
Machine learning
Acute abdomen
Differential diagnosis
Computed tomography
How to Cite
Copyright © | All rights reserved.
The journal holds the exclusive copyright for the publication of this article under the terms of Brazilian law 9610/98.
Partial reproduction
The use of parts of the texts, figures and questionnaire of the article is free, being mandatory the citation of the authors and journal.
Total reproduction
It is expressly prohibited and must be authorized by the journal.
Abstract
Objective: To synthesize the evidence on the use of artificial intelligence and machine learning in differential diagnosis, risk stratification, and clinical and surgical decision support in patients with acute abdomen. Methods: This is an integrative review conducted in accordance with the PRISMA guidelines. The search was performed in the PubMed database using descriptors related to artificial intelligence, machine learning, diagnosis, and acute abdomen. Studies published between 2023 and 2026 that evaluated the application of these technologies in patients with acute abdomen were included. Results: Eleven studies were selected, demonstrating promising results in the identification of acute abdominal diseases, risk stratification, prediction of sepsis, mortality, and surgical complications, as well as support for the interpretation of imaging examinations. The models showed good performance across different clinical and surgical settings. Final considerations: Artificial intelligence and machine learning have the potential to assist in differential diagnosis and decision-making in the management of acute abdomen, serving as complementary tools to clinical assessment. However, further studies are needed for the clinical and external validation of these models.
References
BETTE S, et al. Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography. Diagnostics (Basel), 2024; 14(7): 718.
BMJ Best Practice. Abdominal pain. BMJ Best Practice, 2026.
BRODER J, et al. Guidelines for Reasonable and Appropriate Care in the Emergency Department 2 (GRACE-2): Low-risk, recurrent abdominal pain in the emergency department. Academic Emergency Medicine, 2022; 29(7): 869-882.
CHEN B, et al. Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study. International Journal of Surgery, 2024; 110(6): 3527-3535.
CHOI HU, et al. Diagnostic performance and image quality of an image-based denoising algorithm applied to radiation dose-reduced CT in diagnosing acute appendicitis. Abdominal Radiology, 2024; 49(6): 1839-1849.
DU L, et al. Development and validation of a machine learning-based nomogram model integrating LASSO regression and multiple evaluation methods for predicting early sepsis risk in acute abdomen. European Journal of Medical Research, 2026; 31: 464.
GAN T, et al. Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study. Frontiers in Medicine, 2024; 11: 1354925.
HAMEDI Z, et al. AI-Based Analysis of Abdominal Ultrasound Images to Support Medical Diagnosis in Emergency Departments. Studies in Health Technology and Informatics, 2025; 325: 16-21.
HEEN AF, et al. Machine Learning for Decision-Support in Acute Abdominal Pain: Proof of Concept and Central Considerations. Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2023.
ISSAIY, M., et al., Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models. World Journal of Emergency Surgery, 2023; 18(1): 59.
KIRUBARAJAN A, et al. Artificial intelligence in emergency medicine: A scoping review. Journal of the American College of Emergency Physicians Open, 2020; 1(6): 1691-1702.
LIU Z, et al. Intelligent prediagnosis for nontraumatic acute abdomen with surface-level information using machine learning. Science Progress, 2025; 108(2): 368504251350763.
NEWMAN-TOKER DE, et al. Diagnostic Errors in the Emergency Department: A Systematic Review. Diagnosis, 2022.
PIEBPIEN P, et al. Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models? Journal of International Medical Research, 2024; 52(11): 3000605241293696.
RAGAB H, et al. Artificial Intelligence in Abdominal MRI Diagnostics: Current Applications, Challenges, and Future Perspectives. Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2026.
ROWE SP, et al. The future of radiology: What if artificial intelligence is really as good as predicted? Diagnostic and Interventional Imaging, Volume 103, Issue 9, 2022.
SARTELLI M, et al. The management of intra-abdominal infections from a global perspective: 2017 WSES guidelines for management of intra-abdominal infections. World Journal of Emergency Surgery, 2017; 12: 29.
SINGER M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 2016; 315(8): 801-810.
TAKAISHI T, et al. Deep learning for appendicitis: development of a three-dimensional localization model on CT. Japanese Journal of Radiology, 2025; 43(11): 1859-1869.
TOPOL EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 2019; 25(1): 44-56.
XIANG K, SHANG D. Construction of a Multi-View Deep Learning Model for the Severity Classification of Acute Pancreatitis. Discovery Medicine, 2025; 37(192): 73-92.
WEI W, et al. Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques. Scientific Reports, 2024; 14(1): 16473.
WORLD HEALTH ORGANIZATION. Ethics and Governance of Artificial Intelligence for Health: WHO Guidance. Geneva: World Health Organization, 2021.
WOLFE JM, et al. Abdominal Pain in the Emergency Department: How to Select the Correct Imaging for Diagnosis. Open Access Emergency Medicine, 2022; 14: 789-800.