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Ulmus davidiana has been used as food or medicine in oriental due to its safety, including its roots, bark, and fruits. The roots and bark of Ulmus were used as a diuretic and for treatment due to their anti-cancer, antioxidant, and anti-inflammatory effects. Ulmus species are known to contain flavan-3-ols as their main component. In previous research the presence of (+)-catechin 5-O-β-D-apiofuranoside was isolated from the roots of Ulmus davidiana, and (+)-catechin 7-O-β-D-xylopyranoside was confirmed in the bark Ulmus Americana, and the presence of catechin 7-O-β-D apiofuranoside in the stems and bark of Ulmus davidiana var. japonica. The catechin-glycosides of flavan-3-ols have various effects, including antiulcer properties, and have also been used as analgesics. Additionally, chemotaxonomy studies of the Ulmus species have been reported. However, the chemotaxonomy of Ulmus species native to Asia has not been reported. In this study, we report the screening of catechin 7-O-beta-D apiofuranoside from various Ulmus species native to Asia. We confirmed the presence of catechin 7-O-β-D-apiofuranoside in a total of 22 Ulmus samples, including 18 Ulmus species native to China, and 1 sample each from Mongolian, Nicaragua, Vietnam, and Korea through HPLC and LC-MS/MS analysis. We also confirmed the content of the indicator materials. Therefore, the potential of using native Ulmus species from Korea as a natural material has been confirmed.

This study evaluates the diagnostic accuracy and clinical utility of two artificial intelligence (AI) techniques: Kakao Brain Artificial Neural Network for Chest X-ray Reading (KARA-CXR), an assistive technology developed using large-scale AI and large language models (LLMs), and ChatGPT, a well-known LLM. The study was conducted to validate the performance of the two technologies in chest X-ray reading and explore their potential applications in the medical imaging diagnosis domain. The study methodology consisted of randomly selecting 2000 chest X-ray images from a single institution’s patient database, and two radiologists evaluated the readings provided by KARA-CXR and ChatGPT. The study used five qualitative factors to evaluate the readings generated by each model: accuracy, false findings, location inaccuracies, count inaccuracies, and hallucinations. Statistical analysis showed that KARA-CXR achieved significantly higher diagnostic accuracy compared to ChatGPT. In the ‘Acceptable’ accuracy category, KARA-CXR was rated at 70.50% and 68.00% by two observers, while ChatGPT achieved 40.50% and 47.00%. Interobserver agreement was moderate for both systems, with KARA at 0.74 and GPT4 at 0.73. For ‘False Findings’, KARA-CXR scored 68.00% and 68.50%, while ChatGPT scored 37.00% for both observers, with high interobserver agreements of 0.96 for KARA and 0.97 for GPT4. In ‘Location Inaccuracy’ and ‘Hallucinations’, KARA-CXR outperformed ChatGPT with significant margins. KARA-CXR demonstrated a non-hallucination rate of 75%, which is significantly higher than ChatGPT’s 38%. The interobserver agreement was high for KARA (0.91) and moderate to high for GPT4 (0.85) in the hallucination category. In conclusion, this study demonstrates the potential of AI and large-scale language models in medical imaging and diagnostics. It also shows that in the chest X-ray domain, KARA-CXR has relatively higher accuracy than ChatGPT.