Post

New research · Emergency Medicine
Acute medicine & surgery · 1d
AI / informaticsAcute medicine & surgery · 2026

Impact of Artificial Intelligence Assistance on Diagnosing Traumatic Pneumothorax: A Comparison of Specialist and Non-Specialist Emergency Physicians.

Keisuke Suzuki, Kaede Hiruma, Kazuyuki Miyamoto … Kenji Dohi
Read paper
Emergency MedicineAI / informatics

Artificial intelligence assistance was associated with improved diagnostic accuracy for traumatic pneumothorax.

Impact of Artificial Intelligence Assistance on Diagnosing Traumatic Pneumothorax: A Comparison of Specialist and Non-Specialist Emergency Physicians.

Keisuke Suzuki et al. · Acute medicine & surgery · 2026
Background

Supine chest radiography is routinely used in trauma care; however, its sensitivity is limited in pneumothorax detection.

Purpose

We aimed to evaluate the diagnostic performance of AI-assisted image interpretation in traumatic pneumothorax detection among emergency medicine specialists and non-specialists.

Methods

In this retrospective single-center study, 34 supine chest radiographs (17 pneumothorax and 17 non-pneumothorax cases confirmed with computed tomography) were interpreted by 20 emergency medicine physicians (10 specialists and 10 non-specialists).

n = 34 supine chest radiographs
odds ratio 2.37
Results
Artificial intelligence assistance more than doubled the chances of correctly identifying a collapsed lung from injury
n = 34 supine chest radiographs
More results

AI assistance significantly improved sensitivity and diagnostic accuracy across participants, whereas specificity and precision were not significantly affected.

Specialists demonstrated higher baseline sensitivity and accuracy than non-specialists.

More results

Additionally, the interaction between AI assistance and physician specialist status was significant ( p = 0.010).

“
Conclusion

AI-assisted interpretation of supine chest radiographs is a potentially useful decision-support tool for detecting traumatic pneumothorax in emergency settings.

Read paper
0 comments

No comments yet. Be the first.

Related papers

LatestFoundational
Cross-sectional
62%
most clinicians were interested in a research study for lower leg skin infection
Cohort Study
76%
76% of patients offered virtual care were successfully diverted from the physical emergency department.
AI / Informatics
0.844
how well the new model predicted septic shock
Cohort Study
IRR 0.21
Community Care Team Transitional Care Programme was associated with a lower 90-day death rate
Cohort Study
29.0 min
Patients during unscheduled drills had an average door-to-triage time of 29.0 minutes.
Guideline
5 days
new guidelines recommend less antibiotic treatment than this for nonsevere lung infection
AI / Informatics
79.4%
the model correctly identified the right diagnosis for brain and nerve emergencies