<ul><li>AI hasn't replaced radiologists but has started changing their job by flagging critical findings, ensuring quality control, providing quantification, and assisting in double-reading in high-volume settings.</li><li>Radiologists now work as reviewers, interpreters of AI outputs, focusing on edge cases and interdisciplinary nuances, and working faster and more accurately with machines in the loop.</li><li>Challenges in AI adoption in radiology include false positives, lack of explainability, trust issues, and unclear cost ROI, especially in underfunded public systems.</li><li>Builders of AI for radiology are advised to solve workflow challenges, build for real-world messiness, integrate AI into existing systems, and focus on practical outcomes like time saved, cases flagged, and errors reduced.</li></ul>

What AI is Actually Doing in Radiology Right Now

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