• Journal of Gifted/Talented Education
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• v.21 no.1
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• pp.57-81
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• 2011

The purpose of this study was to explore the quality of life of musically talented students as measured by their external experiences (e.g., activities, companions) and internal experiences (e.g., flow, emotion). The participants in this study were 33 musically talented students (10 males, 23 females) aged 13 to 19. Study data were collected for 7 consecutive days using the Experience Sampling Method (ESM), which employs a cellular-phone as a signaling device. The results were as follows: First, in response to the 1625 random signals, musically talented students reported that 40.9% of their time was spent on productive activities. An additional 33.4% of time was used for maintenance activities and the rest of their time was spent on leisure/social activities. Also, musically talented students reported that 48.5% of their time was spent alone. When they were alone, they spent a lot of time engaging in productive activities (44.3%). Second, in order to measure the flow of their life, two methods were used. One used a 4-channel flow model (i.e. apathy, boredom, flow, anxiety) and the other used 8 dimensions and conditions of the flow experience (i.e. concentration, self-consciousness disappears, action and awareness merge, distorted sense of time, freedom from worry about failure, clear goals, immediate feedback, balance between challenges and skills). According to the former, when engaged in music-related activities, musically talented students usually reported flow (54.0%), while they felt apathy (41.3%) for daily routines activities. According to the latter method, musically talented students experienced flow for most productive activities, while they experienced flow least for maintenance activities. Emotional variables of ESF are comprised of 10 semantic scales (i.e. happy-sad, strong-weak, active-passive, sociablelonely, proud-ashamed, involved-detached, excited-bored, clear-confused, relaxed-worried, cooperative-competitive). Musically talented students reported experiencing the most positive emotion for social activities and experiencing the most negative emotion for maintenance activities. Results of this study assert that musically talented students had to trade off immediate enjoyment for developing their special gifts. They could not afford as much time for socializing with friends, and they had to spend more time alone compared to their peers without such gifts. Consequently, they were found to deprive themselves of the spontaneous good times that teenagers usually thrive on. They were helped in this respect by their autotelic personality traits, especially their strong need for achievement and endurance. The downside, however, is that the moment-to-moment quality of their moods suffered. The argument concerning musically talented students applies for all adolescents. The choices that talented students must make between immediate gratification and long-term development, and between solitude and companionship, are the same choices every young person must make, regardless of her or his level of talent. All of us have gifts that are potentially useful and worthy of being appreciated. But to develop these latent talents we must cultivate them, and this takes time and the investment of mental energy. The lifestyle that musically talented students develop can show us some of the choices all of us must make in order to cultivate our gifts.

• The Journal of Korean Society for Radiation Therapy
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• v.29 no.1
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• pp.49-56
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• 2017

Purpose: To evaluate the accuracy of the Iterative Metal Artifact Reduction (IMAR) algorithm in correcting CT (computed tomography) images distorted due to a metal artifact and to evaluate the usefulness when proton therapy plan was plan using the images on which IMAR algorithm was applied. Materials and Methods: We used a CT simulator to capture the images when metal was not inserted in the CIRS model 062 Phantom and when metal was inserted in it and Artifact occurred. We compared the differences in the CT numbers from the images without metal, with a metal artifact, and with IMAR algorithm by setting ROI 1 and ROI 2 at the same position in the phantom. In addition, CT numbers of the tissue equivalents located near the metal were compared. For the evaluation of Rando Phantom, CT was taken by inserting a titanium rod into the spinal region of the Rando phantom modelling a patient who underwent spinal implant surgery. In addition, the same proton therapy plan was established for each image, and the differences in Range at three sites were compared. Results: In the evaluation of CIRS Phantom, the CT numbers were -6.5 HU at ROI 1 and -10.5 HU at ROI 2 in the absence of metal. In the presence of metal, Fe, Ti, and W were -148.1, -45.1 and -151.7 HU at ROI 1, respectively, and when the IMAR algorithm was applied, it increased to -0.9, -2.0, -1.9 HU. In the presence of metal, they were 171.8, 63.9 and 177.0 HU at ROI 2 and after the application of IMAR algorithm they decreased to 10.0 6,7 and 8.1 HU. The CT numbers of the tissue equivalents were corrected close to the original CT numbers except those in the lung located farthest. In the evaluation of the Rando Phantom, the mean CT numbers were 9.9, -202.8, and 35.1 HU at ROI 1, and 9.0, 107.1, and 29 HU at ROI 2 in the absence, presence of metal, and in the application of IMAR algorithm. The difference between the absence of metal and the range of proton beam in the therapy was reduced on the average by 0.26 cm at point 1, 0.20 cm at point 2, and 0.12 cm at point 3 when the IMAR algorithm was applied. Conclusion: By applying the IMAR algorithm, the CT numbers were corrected close to the original ones obtained in the absence of metal. In the beam profile of the proton therapy, the difference in Range after applying the IMAR algorithm was reduced by 0.01 to 3.6 mm. There were slight differences as compared to the images absence of metal but it was thought that the application of the IMAR algorithm could result in less error compared with the conventional therapy.

• Journal of Oral Medicine and Pain
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• v.31 no.1
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• pp.59-67
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• 2006

There exist patients complaining speech problem due to dysesthesia or anesthesia following dental surgical procedure accompanied by local anesthesia in clinical setting. However, it is not clear whether sensory problems in orofacial region may have an influence on motor speech abilities. The purpose of this study was to investigate whether transitory sensory impairment of mandibular nerve by local anesthesia may influence on the motor speech abilities and thus to evaluate possibility of distorted motor speech abilities due to dysesthesia of mandibular nerve. The subjects in this study consisted of 7 men and 3 women, whose right inferior alveolar nerve, lingual nerve and long buccal nerve was anesthetized by 1.8 mL lidocaine containing 1:100,000 epinephrine. All the subjects were instructed to self estimate degree of anesthesia on the affected region and speech discomfort with VAS before anesthesia, 30 seconds, 30, 60, 90, 120 and 150 minutes after anesthesia. In order to evaluate speech problems objectively, the words and sentences suggested to be read for testing speech speed, diadochokinetic rate, intonation, tremor and articulation were recorded according to the time and evaluated using a Computerized Speech $Lab^{(R)}$. Articulation was evaluated by a speech language clinician. The results of this study indicated that subjective discomfort of speech and depth of anesthesia was increased with time until 60 minutes after anesthesia and then decreased. Degree of subjective speech discomfort was correlated with depth of anesthesia self estimated by each subject. On the while, there was no significant difference in objective assessment item including speech speed, diadochokinetic rate, intonation and tremor. There was no change in articulation related with anesthesia. Based on the results of this study, it is not thought that sensory impairment of unilateral mandibular nerve deteriorates motor speech abilities in spite of individual's complaint of speech discomfort.

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