Nodule AI cut chest CT reporting time by 15%, but not evenly

This was a retrospective before-and-after study at one tertiary centre, comparing 39,323 chest CT examinations either side of the introduction of a commercial pulmonary-nodule AI tool (19,190 before, 20,133 after). Reporting time was modelled with adjustment for reader, examination type, patient location and requesting specialty.

After AI, adjusted median reporting time fell from 21.3 to 18.2 minutes, a 14.6% reduction (adjusted hazard ratio for report completion 1.17, 95% CI 1.14 to 1.21). The effect was not uniform: ECG-gated thoracic CT was reported 41% faster and thoracic radiologists gained 25%, while emergency department examinations were actually 7% slower. Exploratory modelling suggested roughly half a full-time radiologist of freed capacity at that centre's volumes.

The honest reading is that a nodule tool helps most where nodules are the question, and can get in the way where they are not. A before-and-after design cannot separate the software from three years of other change, so treat the 15% as an association, not a guaranteed return.

• The measured gain was throughput, not diagnostic accuracy — which this study did not assess
• Largest benefit in nodule-focused work (ECG-gated thoracic CT, thoracic radiologists)
• Emergency department reporting was slower after AI, so weigh where you deploy it
• Pre/post design: unmeasured change over 2021–2024 could account for some of the effect

The statistics, in plain English

An adjusted hazard ratio of 1.17 for report completion means reports were finished about 17% faster per unit time after AI; because the confidence interval (1.14 to 1.21) sits well above 1.0, the speed-up is statistically clear. But a before-and-after comparison is weak causal evidence: anything else that changed between the two periods — staffing, case mix, other software — is bundled into that number.

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