Artificial Intelligence in Soft Tissue Tumor Evaluation in View of Radiological Images Evaluation: Systematic Review and Meta-Analysis

Introduction: In the field of oncological radiography imaging, artificial intelligence (AI) plays pivotal role, aiding both diagnosis and treatment. While computer-aided diagnosis has been employed in cancer imaging previously, it has shown limited clinical value; however, AI-based models are now routinely part of body organ imaging and are clinically used in many circumstances. The present study aimed to evaluate the diagnostic accuracy of AI in soft tissue tumor evaluation in view of radiological images evaluation. Methodology: The present systematic review and meta-analysis included seven cohort studies of international databases, PubMed, Scopus, Web of Science, and Embase, from January 1, 2015, to May 10, 2025, using keywords aligned with the study objective. The statistical analysis was performed with Stata/MP.v17 as fixed effect models. Results: The accuracy of AI in soft tissue tumor assessment based on radiological images was 82% (ES = 0.82; 95% CI 0.68-0.96). The sensitivity and specificity of AI in soft tissue tumor assessment based on radiological images were 74%, and 88%, respectively. Conclusion: AI tools related to imaging with high accuracy, sensitivity, and acceptable specificity can take positive steps toward helping in the diagnosis of soft tissue tumors.