Artificial Intelligence

Radiology NLP’s Efficiency and Accuracy Potential

The last week brought two high profile studies underscoring radiology NLP’s potential to improve efficiency and accuracy, showing how the language-based technology can give radiologists a reporting head-start and allow them to enjoy the benefits of AI detection without the disruptions.

AI + NLP for Nodule QA – A new JACR study detailed how Yale New Haven Hospital combined AI and NLP to catch and report more incidental lung nodules in emergency CT scans, without impacting in-shift radiologists. The quality assurance program used a CT AI algorithm to detect suspicious nodules and an NLP tool to analyze radiology reports, flagging only the cases that AI marked as suspicious but the NLP tool marked as negative.

  • The AI/NLP program processed 19.2k CT exams over an 8-month period, flagging just 50 cases (0.26%) for a second review.
  • Those flagged cases led to 34 reporting changes and 20 patients receiving follow-up imaging recommendations. 
  • Just as notably, this semi-autonomous process helped rads avoid “thousands of unnecessary notifications” for non-emergent nodules.

NLP Auto-Captions – JAMA highlighted an NLP model that automatically generates free-text captions describing CXR images, streamlining the radiology report writing process. A Shanghai-based team trained the model using 74k unstructured CXR reports labeled for 23 different abnormalities, and tested with 5,091 external CXRs alongside two other caption-generating models.

  • The NLP captions reduced radiology residents’ reporting times compared to when they used a normal captioning template or a rule-based captioning model (283 vs. 347 & 296 seconds), especially with abnormal exams (456 vs. 631 & 531 seconds). 
  • The NLP-generated captions also proved to be most similar to radiologists’ final reports (mean BLEU scores: 0.69 vs. 0.37 & 0.57; on 0-1 scale).

The Takeaway

These are far from the first radiology NLP studies, but the fact that these implementations improved efficiency (without sacrificing accuracy) or improved accuracy (without sacrificing efficiency) deserves extra attention at a time when trade-offs are often expected. Also, considering that everyone just spent the last month marveling at what ChatGPT can do, it might be a safe bet that even more impressive language and text-based radiology solutions are on the way.

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