• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.63-79
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• 2024

The startup ecosystem is experiencing a paradigm shift in founding due to the acceleration of digital transformation, online platform companies have grown significantly into unicorns, but the lack of differentiated approaches and strategic support for deep tech startups has led to the inactivity of the startup ecosystem. is lacking. Therefore, in this study, we proposed ways to develop domestic startup development policies, focusing on the US system, which is an advanced example overseas. Focusing on the definition and characteristics of deep tech startups, current investment status, success stories, support policies, etc., we comprehensively analyzed domestic and international literature and derived suggestions. In particular, he proposed specific ways to improve support policies for domestic deep tech startups and presented milestones for their development. Currently, the United States is significantly strengthening the role of the government in supporting deep tech startups. The US government provides direct financial support to deep tech startups, including detergent support and infrastructure support. It has also established policies to foster deep tech startups, established related institutions, and systematized support. It is worth noting that US universities play a core role in nurturing deep tech startups. Leading universities in the United States operate deep tech startup discovery and development programs, providing research and development infrastructure and technology. It also works with companies to provide co-investment and commercialization support for deep tech startups. As a result, the growth of domestic deep tech startups requires the cooperation of diverse entities such as the government, universities, companies, and private investors. The government should strengthen policy support, and universities and businesses should work together to support R&D and commercialization capabilities. Furthermore, private investors must stimulate investment in deep tech startups. Through such efforts, deep tech startups are expected to grow and Korea's innovation ecosystem will be revitalized.

• 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.