From Anatomy to Pathology: How Well Can AI Interpret Static Point-of-Care Ultrasound Images?

Background/Objectives: Multimodal artificial intelligence may support point-of-care ultrasound (POCUS) interpretation in emergency medicine, but its performance on static images and sensitivity to acquisition context remain uncertain. This study evaluated GPT-5.6 Sol and assessed whether probe-region information improves interpretation across view/plane, anatomy, and pathology domains. Methods: In this paired comparative in silico study, 92 educational ultrasound images from seven anatomical regions were assessed under standardized uninformed (image only) and informed (image plus probe-region code) prompting conditions. Two independent raters scored responses against textbook-based references in three domains: view/plane identification, anatomical structure identification, and pathological finding detection. Each domain was scored from 0 to 2. Disagreements were resolved by a third ultrasound expert, and fully correct responses were defined as domain scores of 2. The primary outcome was the total performance score (0–6). Secondary outcomes included domain-specific scores, fully correct response rates, and exploratory subgroup analyses. Wilcoxon signed-rank and McNemar tests assessed paired scores and fully correct rates, respectively. Results: Total performance was higher with informed prompting (median (interquartile range), 5 (4–6) versus 4 (2–5); p < 0.001). Interrater agreement was substantial to almost perfect across evaluation domains (quadratic-weighted κ = 0.750–1.000). Fully correct rates increased from 58.7% to 88.0% for view/plane identification and from 47.8% to 82.6% for anatomical structure identification (both p < 0.001). Pathological finding detection improved from 35.9% to 48.9% (p = 0.002). Among pathology-positive images, missed or incorrectly labeled pathology remained frequent (44.8%) despite informed prompting. Regional differences in total performance were significant only under uninformed prompting (p = 0.002). Conclusions: Probe-region information substantially improved view/plane and anatomical structure identification, but pathological finding detection remained comparatively weak. In static POCUS interpretation, improved anatomical recognition did not translate into reliable pathology detection. Persistent false-negative and false-positive errors do not support independent diagnostic use. Potential applications in POCUS education and anatomical labeling require further validation.