Can Artificial Intelligence Really Help? A Practicing Radiologist’s Simplified Guide to AI, with a Critical Appraisal of the Use of AI in Cancer-Associated Thromboembolism

Abstract

Artificial intelligence (AI) has generated considerable excitement in radiology, with claims of transformative improvements in diagnostic accuracy, workflow efficiency, and clinical decision support. However, a critical gap persists between AI’s theoretical promise and its real-world performance, particularly in complex, high-stakes scenarios such as cancer-associated thromboembolism (CAT). CAT is a leading cause of morbidity and mortality in oncology patients, yet it remains underdiagnosed on routine imaging. This paper provides a general radiology critique of current AI applications, then narrows focus to CAT management. This review additionally evaluates AI’s role in incidental pulmonary embolism detection, risk stratification, and treatment decision support. While AI demonstrates sensitivity gains, it faces substantial limitations: data heterogeneity, lack of prospective validation, poor generalizability across cancer subtypes, and integration challenges with clinical workflows. Therefore, AI is not yet a reliable standalone tool for CAT management, but may serve as an adjunct if clinically validated, explainable, and embedded within multidisciplinary frameworks.