Commercial clot-detection AI missed the cases that were easiest to overlook
Across more than 67,000 CT examinations, two FDA-cleared models were highly specific but less sensitive than their clearance studies suggested—especially for smaller, older and incidental clots.
The short version
- What happened
- The retrospective study evaluated 30,678 dedicated CT pulmonary angiograms and 37,191 routine contrast-enhanced CT examinations across 17 facilities from April 2023 to April 2025.
- Who it affects
- For a patient, the practical risk is false reassurance: the commercial system was least dependable for subtle or older clots. Radiologists still need to inspect the scan and clinical context even when the AI returns no alert.
- What remains unproven
- This was retrospective work in one academic health system, albeit across 17 facilities; community and rural performance may differ.
Keep in mind
This was retrospective work in one academic health system, albeit across 17 facilities; community and rural performance may differ.
The main reference labels came from radiology reports exposed to the AI output, not an independent blinded image-review panel.
Suboptimal and indeterminate examinations were excluded, and software-version differences make the FDA benchmark illustrative rather than like-for-like.
No causal patient outcome—such as time to treatment, complications or mortality—was measured.
Source analysis · 2 linked sources. Primary analysed source date: 21 October 2026. Earliest linked supporting source: 29 September 2026.
Read the full report · 3 min The evidence, methods, implications and limitations
High specificity hid a clinically important miss rate
Researchers at Emory evaluated two Aidoc algorithms already running in clinical practice. On 30,678 dedicated scans, the pulmonary-embolism model reached 86.8% sensitivity and 99.1% specificity. Its sensitivity was below the 94.9% reported in the FDA clearance summary, although the study populations and software versions may not have been identical.
The gap widened where help may be most valuable. Sensitivity declined from 99.3% for saddle emboli to 72.9% for subsegmental emboli, and from 89.7% for acute clots to 65.3% for non-acute ones. A negative flag therefore could not safely end a radiologist's search for a subtle clot.[1][2]
Incidental clots were a harder test
A separate algorithm examined 37,191 routine contrast-enhanced chest and abdominal CT studies, where pulmonary embolism was not the reason for scanning. It detected 336 of 457 confirmed incidental emboli: 73.5% sensitivity, alongside 99.8% specificity.
In a selected subset later given dedicated imaging, it had flagged 42 of 61 emboli confirmed on follow-up and missed 19. That supports using the tool as a second look, not an autonomous clearance step. The study did not measure whether any flag prevented harm or accelerated treatment.[2]
The distinction that matters
Where the algorithm's sensitivity fell
The same system was much more likely to flag a large central clot than a small peripheral or older one.
Saddle clot
Run the dedicated PE model
99.3% sensitivity
Subsegmental clot
Run the same model
72.9% sensitivity
Incidental clot
Run the routine-CT model
73.5% sensitivity
The real-world label was not fully independent
The team extracted labels from final radiology reports with a validated language-model pipeline that achieved 97% accuracy and a kappa of 0.94 against expert labels. But clinicians could see the AI flag, so the reference standard may favour the deployed system. There was no blinded image re-adjudication of every discordant case.
The work was NIH-funded in part; Aidoc supplied aggregated prediction outputs but the authors say it had no role in design, analysis or writing. Prospective, independently adjudicated studies across unrelated health systems—and patient outcomes rather than detection metrics—would change the assessment most.[2]