Addressing Bias and Methodological Challenges in AI Radiology Meta-Analyses

Meta-analyses of AI in radiology are often biased and methodologically flawed; authors call for more rigorous standards to improve reliability of pooled estimates.

• The paper identifies publication bias, heterogeneous study designs, and selective reporting as key challenges in meta-analyses of AI diagnostic accuracy.
• Proposes a framework for future meta-analyses to enhance transparency and reproducibility.
• Limitation: Perspective piece without new empirical data; recommendations need validation in future meta-analytic research.

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