Electronics and Telecommunications Trends (전자통신동향분석)

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Technical Trends of Medical AI Hubs

의료 AI 중추 기술 동향

  • Published : 2021.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

Post COVID-19, the medical legacy system will be transformed for utilizing medical resources efficiently, minimizing medical service imbalance, activating remote medical care, and strengthening private-public medical cooperation. This can be realized by achieving an entire medical paradigm shift and not simply via the application of advanced technologies such as AI. We propose a medical system configuration named "Medical AI Hub" that can realize the shift of the existing paradigm. The development stage of this configuration is categorized into "AI Cooperation Hospital," "AI Base Hospital," and "AI Hub Hospital." In the "AI Hub Hospital" stage, the medical intelligence in charge of individual patients cooperates and communicates autonomously with various medical intelligences, thereby achieving synchronous evolution. Thus, this medical intelligence supports doctors in optimally treating patients. The core technologies required during configuration development and their current R&D trends are described in this paper. The realization of the central configuration of medical AI through the development of these core technologies will induce a paradigm shift in the new medical system by innovating all medical fields with influences at the individual, society, industry, and public levels and by making the existing medical system more efficient and intelligent.

Keywords

References

  1. S. Angela, H. Solveigh, and J. Jonathan, "Transforming healthcare with AI: The impact on the workforce and organizations," McKinsey & Company, Healthcare Systems & Services, 2020.
  2. 김동철, "[특별기고] 인공지능(AI)시대의 도래: 병리과의 현실과 미래," 헬스경향, 2019. 3. 8.
  3. E. Tjoa and C. Guan, "A survey on explainable artificial intelligence (XAI): Toward medical XAI," IEEE Trans. Neural. Netw. Learn. Syst. 2020, pp. 1-21.
  4. IRS Global, "헬스케어.의료 분야 인공지능(AI)과 빅데이터(Big Data)의 핵심기술 개발동향과 국내외 시장 분석," 2019.
  5. E. Strickland, "IBM Watson, heal thyself: How IBM overpromised and underdelivered on AI health care," IEEE Spectr. vol. 56, no. 4, 2019, pp. 24-31. https://doi.org/10.1109/mspec.2019.8678513
  6. Q. Yang et al., "Federated machine learning: Concept and applications," ACM Trans. Intell. Syst. Technol. vol. 10, no. 2. 2019.
  7. R. S. Sutton and A. G. Barto, "Introduction to reinforcement learning," The MIT Press, 2018.
  8. O. Gottesman et al., "Guidelines for reinforcement learning in healthcare," Nat. Med. vol. 25, 2019, pp. 16-18. https://doi.org/10.1038/s41591-018-0310-5