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Objectives There is emerging use of artificial intelligence (AI) models to aid diagnostic imaging. This review examined and critically appraised the application of AI models to identify surgical pathology from radiological images of the abdominopelvic cavity, to identify current limitations and inform future research.Design Systematic review.Data sources Systematic database searches (Medline, EMBASE, Cochrane Central Register of Controlled Trials) were performed. Date limitations (January 2012 to July 2021) were applied.Eligibility criteria Primary research studies were considered for eligibility using the PIRT (participants, index test(s), reference standard and target condition) framework. Only publications in the English language were eligible for inclusion in the review.Data extraction and synthesis Study characteristics, descriptions of AI models and outcomes assessing diagnostic performance were extracted by independent reviewers. A narrative synthesis was performed in accordance with the Synthesis Without Meta-analysis guidelines. Risk of bias was assessed (Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2)).Results Fifteen retrospective studies were included. Studies were diverse in surgical specialty, the intention of the AI applications and the models used. AI training and test sets comprised a median of 130 (range: 5–2440) and 37 (range: 10–1045) patients, respectively. Diagnostic performance of models varied (range: 70%–95% sensitivity, 53%–98% specificity). Only four studies compared the AI model with human performance. Reporting of studies was unstandardised and often lacking in detail. Most studies (n=14) were judged as having overall high risk of bias with concerns regarding applicability.Conclusions AI application in this field is diverse. Adherence to reporting guidelines is warranted. With finite healthcare resources, future endeavours may benefit from targeting areas where radiological expertise is in high demand to provide greater efficiency in clinical care. Translation to clinical practice and adoption of a multidisciplinary approach should be of high priority.PROSPERO registration number CRD42021237249.

Introduction The application of artificial intelligence (AI) technologies as a diagnostic aid in healthcare is increasing. Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may improve the performance of diagnostic, prognostic and management decisions. While a large amount of work has been undertaken discussing the role of AI little is understood regarding the performance of such applications in the clinical setting. This systematic review aims to critically appraise the diagnostic performance of AI algorithms to identify disease from cross-sectional radiological images of the abdominopelvic cavity, to identify current limitations and inform future research.Methods and analysis A systematic search will be conducted on Medline, EMBASE and the Cochrane Central Register of Controlled Trials to identify relevant studies. Primary studies where AI-based technologies have been used as a diagnostic aid in cross-sectional radiological images of the abdominopelvic cavity will be included. Diagnostic accuracy of AI models, including reported sensitivity, specificity, predictive values, likelihood ratios and the area under the receiver operating characteristic curve will be examined and compared with standard practice. Risk of bias of included studies will be assessed using the QUADAS-2 tool. Findings will be reported according to the Synthesis Without Meta-analysis guidelines.Ethics and dissemination No ethical approval is required as primary data will not be collected. The results will inform further research studies in this field. Findings will be disseminated at relevant conferences, on social media and published in a peer-reviewed journal.PROSPERO registration number CRD42021237249.