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According to AI TIMES, Daedong said on October 6 that it will operate an AI tractor test-drive program centered on 29 dealerships nationwide. The event is designed to let farmers drive the tractors themselves and check autonomous work functions and usability, and Daedong said it is focusing on expanding customer touchpoints through the end of the year. The company said it opted for an experience-centered approach because simple product explanations or spec comparisons alone make it difficult to convey the strengths of AI agricultural machinery. In particular, it aims to let users assess on-site factors that matter in actual work, such as route setting, autonomous driving methods, and the level of driver intervention. Applications for test drives are being accepted through the company website and customer center, and each regional center will coordinate schedules. Daedong plans to use this as an opportunity to further expand dealership-based hands-on experiences and increase touchpoints where AI-based farm work can take root in the field.

According to AI TIMES, the medical AI companies introduced by NVIDIA on October 5 are expanding AI use in breast cancer care beyond imaging and reading to treatment decisions and surgical planning. In particular, technologies that reduce delays in care, such as automated ultrasound that scans one breast in about 2 minutes, and multimodal approaches that review imaging, pathology, and clinical information together were highlighted as key. The article emphasized that AI is not remaining merely a tool to determine whether cancer is present. It is evolving to improve access to testing and reduce the burden of repeated interpretation, while also predicting treatment response and the likelihood of recurrence to help clinicians make decisions sooner. Efforts to connect scattered patient data into a single analytical framework to build more precise treatment strategies were also notable. However, it was noted that for such technologies to become part of standard care in practice, they will need validation across diverse patient groups and medical institutions, as well as confirmation of how AI results should be reflected in clinical judgment.