TY  - JOUR
T1  - CLASSIFICATION OF CAROTID DOPPLER REPORTS BY LARGE LANGUAGE MODELS: A BRIEF OBSERVATION
TT  - BÜYÜK DİL MODELLERİ İLE KAROTİS DOPPLER RAPORLARININ SINIFLANDIRILMASI: KISA BİR GÖZLEM
AU  - Şalbaş, Ali
PY  - 2025
DA  - July
Y2  - 2025
DO  - 10.26650/IUITFD.1674196
JF  - Journal of Istanbul Faculty of Medicine
JO  - İst Tıp Fak Derg
PB  - İstanbul Üniversitesi
WT  - DergiPark
SN  - 1305-6441
SP  - 247
EP  - 248
VL  - 88
IS  - 3
LA  - en
AB  - Dear Editor,Large language models (LLMs) are increasingly important in clinical decision support systems and medical education due to their ability to analyse medical texts (1). This brief observation evaluates the performance of four LLMs — ChatGPT-4o (OpenAI), Claude 3.7 Sonnet (Anthropic), Gemini 1.5 Pro (Google DeepMind), and Grok-3 (xAI) — in classifying internal carotid artery (ICA) stenosis according to the Society of Radiologists in Ultrasound (SRU) criteria, using velocity parameters in carotid Doppler ultrasonography (USG) reports (2). A total of 40 USG reports were used, all containing identical velocity data but presented in two distinct formats. Each report included the peak systolic velocity (PSV), end diastolic velocity (EDV), and internal carotid artery/common carotid artery (ICA/CCA) PSV ratio for both the right and left ICA. The first 20 reports included non-directive descriptive statements. In the remaining 20, the same velocity values were retained, but directive phrases such as “plaques not causing significant stenosis” and “no haemodynamically significant stenosis detected” were added.
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UR  - https://doi.org/10.26650/IUITFD.1674196
L1  - https://dergipark.org.tr/tr/download/article-file/4764803
ER  -