Experts at RSNA 2025 debated whether AI is ready for fully autonomous interpretation of chest x-rays, concluding that while technical progress is evident, significant challenges remain.
Key Details
- 1Debate featured Saurabh (Harry) Jha, MD, and Warren Gefter, MD, at RSNA 2025, moderated by Eun Kyoung (Amy) Hong, MD, PhD.
- 2AI accuracy and reliability cited as insufficient for fully autonomous chest x-ray reads in most clinical settings.
- 3Key AI challenges include hallucinations (up to 20% of reports), regulatory hurdles, and variability across models.
- 4AI collaboration is effective for normal studies or TB screening but not broad clinical use yet.
- 5A prospective trial showed AI-generated draft reports improved reporting efficiency by 15-20%, saving up to 29 seconds per case.
- 6Panelists stressed the need for robust quality control, drift detection, and clear regulatory frameworks.
- 7Audience poll suggested widespread autonomous AI for CXR could still be 20 years away.
Why It Matters
Assessment of AI's readiness for autonomous x-ray interpretation directly impacts clinical workflow, radiologist responsibilities, and patient safety. The debate underscores the need for ongoing innovation in AI accuracy, regulatory clarity, and partnership with radiologists to safely realize efficiency gains.
Source
AuntMinnie
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