A case report in Emergency Radiology describes exactly the kind of failure that validation literature rarely captures: a chest radiograph algorithm flagged a right apical pneumothorax in an 81-year-old man who had just received a permanent pacemaker on the left side. The care team disagreed with the machine but, out of caution, ordered additional imaging and reassessment. There was no pneumothorax. The case matters less for the false positive itself than for why it happened — the AI had no idea what procedure the patient had undergone.
The case: left-sided pacemaker, right-sided pneumothorax
The protocol was routine. After an uncomplicated left-sided permanent pacemaker implantation, the patient had the standard post-procedural chest radiograph. The study was read in parallel by the care team and by AI software specialized in chest x-ray interpretation. The tool marked the lung fields with automated visual annotation, calling a right apical pneumothorax.
“Artificial intelligence has become increasingly integrated into chest radiograph interpretation, demonstrating excellent diagnostic performance for several thoracic abnormalities, including pneumothorax,” wrote Fulvio Cacciapuoti, MD, of the division of cardiology at Antonio Cardarelli Hospital in Italy, and colleagues. “Nevertheless, current AI systems remain predominantly image-based and do not inherently incorporate procedural or clinical information into diagnostic interpretation.”
Why laterality destroys the hypothesis
Here is the point any on-call radiologist recognizes in three seconds. Pneumothorax is a classic complication of pacemaker implantation — incidence on the order of 1% to 2% — and it happens because the subclavian venous puncture crosses the pleura. It therefore appears on the same side as the venous access. A right apical pneumothorax after a left-sided implant is not the expected procedural complication: it would be an independent event, with no obvious mechanism, in a patient who was just punctured on the opposite side.
That is the information that shifts pre-test probability dramatically. The human radiologist reads the order, sees “post left-sided pacemaker implantation”, and instantly puts the left apex under maximum scrutiny and the right apex under low suspicion. The algorithm, fed only pixels, does not perform that weighting. It computes a probability from the disease distribution of its training set — not from this patient’s history.
What mimics an apical pneumothorax
The mimics are worth spelling out, because they explain why the apex is precisely where false positives proliferate. Skin folds in an elderly, semi-recumbent patient produce sharp lines that cross the lung field and end outside the rib cage — a classic. The companion shadow of the first rib, a poorly positioned medial scapular border, apical emphysematous bullae, subcutaneous emphysema from generator pocket dissection, and the lead and suture wires themselves all belong in the same category.
Then add technique. A post-procedural radiograph is almost always a portable AP, semi-recumbent, with poor inspiration and rotation. Most algorithms were trained predominantly on erect PA views. That distribution change — classic domain shift — degrades performance silently: the accuracy printed in the validation paper is not the accuracy a service gets at the bedside. It is the same argument we made when analyzing why mammography AI delivered less than promised outside the study environment.
The symmetric risk: using AI to rule out
The report was framed as a warning for anyone using AI to exclude suspicious findings, and the logical inversion deserves attention. If a system blind to procedural context can mark a pneumothorax where none exists, it can equally fail to mark one that does exist — and in that scenario the harm is greater, because the absence of an annotation tends to read as reassuring. A negative report built on agreement with the machine is epistemically weaker than a negative report built from the clinical history.
There is also an anchoring effect. A visual annotation overlaid on the image is not neutral: it steers the eye and creates automation bias in both directions — the reader tends to confirm what the box marked and to relax where nothing was marked. When we covered the study on what protects a radiologist from erring alongside the model, the core finding was identical: expertise and context are the antidote, not trust in the system’s output.
What to do in your department
Four concrete measures cut this class of event without requiring new technology. First, enrich the order: post-procedural studies must carry the procedure and the side in the indication field. That is free and solves half the problem for the human reader.
Second, define the software’s scope in governance. Triage devices cleared by the FDA and equivalent agencies are authorized for notification and worklist prioritization — not for primary diagnosis. A service that treats the annotation as a report is using the product off-label. Third, audit by subgroup: splitting performance by view (PA versus portable AP), by positioning and by post-procedural context exposes degradations that aggregate metrics hide. Fourth, log disagreements. Without a simple channel for the radiologist to flag “the AI was wrong here”, no institutional learning curve forms — and the same false positive returns next week.
Limits of the report and what comes next
The case has to be read for what it is: a single report with no denominator. You cannot estimate a false-positive rate from it, and publication bias is obvious — interesting failures get published, routine successes do not. Nothing in it contradicts the evidence that pneumothorax algorithms shorten time to report for critical findings, which is the real benefit of these systems.
The technical path forward is clear, though. The next generation of tools has to consume structured context — indication, recent procedure, laterality, prior studies — instead of pixels alone. Multimodal models that read the chart alongside the image are already technically feasible; the obstacle is regulatory and RIS/PACS integration, not architecture. Until that arrives, the golden rule remains radiology’s oldest: no image is interpreted without the clinical history.
Source: Health Imaging — A cautionary tale for radiologists who use AI to rule out suspicious findings
