The role of artificial intelligence in emergency general surgery: Trends, advances, and future directions

Samarakoon, LB, Correa, E ORCID: 0000-0002-5122-4384 and Chung, WY (2025) The role of artificial intelligence in emergency general surgery: Trends, advances, and future directions. Clinical Surgical Oncology, 4 (4). p. 100103. ISSN 2773-160X

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Abstract

Artificial intelligence (AI) is increasingly integrated into emergency general surgery (EGS), offering advances in diagnosis, decision support, operative planning, intraoperative guidance, and postoperative management. This review synthesises current evidence on AI applications in EGS, drawing on meta-analyses, large-scale datasets, and landmark studies. Key domains include risk prediction, intraoperative assistance, surgical video analysis, training, prehabilitation, and operational coordination. Evidence shows AI can improve diagnostic accuracy, streamline workflows, and enhance patient outcomes, though benefits vary by setting, resource availability, and clinical domain. Adoption is accelerating, supported by rising global funding, yet constrained by regulatory, ethical, and implementation challenges. Addressing these barriers, standardising evaluation metrics, and expanding high-quality, multicentre trials will be essential to realise AI's full potential in EGS.

Item Type:	Article
Uncontrolled Keywords:	46 Information and Computing Sciences; 32 Biomedical and Clinical Sciences; 3202 Clinical Sciences; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; Patient Safety; Clinical Research; Bioengineering; 7.3 Management and decision making; Generic health relevance
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RD Surgery T Technology > T Technology (General)
Divisions:	Computer Science and Mathematics
Publisher:	Elsevier BV
Date of acceptance:	2 November 2025
Date of first compliant Open Access:	26 May 2026
Date Deposited:	26 May 2026 15:00
Last Modified:	26 May 2026 15:00
DOI or ID number:	10.1016/j.cson.2025.100103
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28654

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