ReviewCancers2026
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.
Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.