• The Korean Society of Law and Medicine
• /
• v.24 no.4
• /
• pp.67-101
• /
• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• Korean journal of applied entomology
• /
• v.61 no.2
• /
• pp.319-330
• /
• 2022

Andong is a place to culture the great amount of hot pepper (Capsicum annuum L.) in Korea. This study reports a yearly occurrence (March 31~October 25, 2021) of thrips infesting the hot pepper in Andong. Thrips caught to yellow sticky traps were diagnosed by morphological characters and showed two dominant species: Frankliniella occidentalis and F. intonsa. During this period, a total of 107,874 thrips were caught and included F. occidentalis at about 82%, F. intonsa at about 17%, and the other thrips at about 0.3%. There were two main peaks at May~June and at September~October, respectively, in which the total number of thrips was higher in the second peak and most were F. occidentalis. Interestinly, a low level of thrips occurred during July~August was observed and explained by their susceptibility to high temperatures. A laboratory experiment by exposing thrips to high temperatures showed that thrips were susceptible to temperatures higher than 35℃ and not tolerant to 45℃ for 1 h. Indeed, high temperatures higher than 45℃ were recorded in the greenhouses in Andong during July~August. F. occidentalis was more tolerant to the high temperatures than F. intonsa. On the other hand, the thrips showed the highest occurrence peak at July~August in hot pepper-culturing greenhouse in Kangwon, where the average temperatures were mostly lower than those of Andong and no high temperatures higher than 45℃ were recorded during July~August. A viral disease caused by tomato spotted wilt virus (TSWV) was observed in the hot peppers cultured in Andong greenhouses. Multiplex PCR was used to detect the virus along with identification of thrips. With a high record of about 30%, the virus-infected thrips were detected during all the monitoring period. The virulent thrips were identified to be only F. intonsa. These results suggest that F. occidentalis gives a direct damage especially during harvesting period with their high populations while F. intonsa gives indirect damage by transmitting TSWV.