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A Study on the Development Methodology of Intelligent Medical Devices Utilizing KANO-QFD Model (지능형 메디컬 기기 개발을 위한 KANO-QFD 모델 제안: AI 기반 탈모관리 기기 중심으로)

  • Kim, Yechan;Choi, Kwangeun;Chung, Doohee
    • Journal of Intelligence and Information Systems
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    • v.28 no.1
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    • pp.217-242
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    • 2022
  • With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.