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일반논문

Analysis of Medical Device Class Changes Caused when European Regulation (MDR) is Applied to Classes of Korean Software as a Medical Device (SaMD) Items

초록

영어

At present, the domestic and overseas markets for artificial intelligence (AI)-based Software as a Medical Device (SaMD) services for medical video are rapidly increasing. Moreover, the future is also promising. In South Korea, AI-based software (SW) utilizing video and medical big data accounts for a large proportion of Korean SaMD standard diagnosis/detection assistance SaMD SW approved by Ministry of Food and Drug Safety (MFDS) and used for clinical experiments. Future work will focus on device development. Furthermore, Europe is currently the world’s second-largest market for medical devices. As a result, Korean manufacturers may explore marketing medical products to EU countries. However, the Korean medical device classification system and the European medical device classification system are different. In addition, European regulation is in flux, with an old standard (MDD) currently being updated to a new one (MDR); this presents additional factors that SaMD producers should consider. This paper presents the medical device grades that should be considered first for export. In addition, this paper also explains which provisions applicable to SaMD exist in MDR, as well as how Korean medical device (i.e., Korean SaMD standard diagnostic and detection assistance software) grades will change according to the new European legal standard (MDR).

목차

Abstract
I. Introduction
II. Analysis of Medical Device
1. Korean medical device classification system
2. SaMD classification by item in South Korea
3. The European MDSW classification system
4. SaMD of MDR: classification of SaMD: Implementing Rules
5. MDR classification of SaMDs: Classification Rules
III. Re-classification of Korean SaMD according the MDR classification
1. Endoscopic imaging and computer-aided detection/diagnosis software, Class 2: Class IIa
2. Ultrasound imaging and computer-aided detection/diagnosis software, Class 2: Class IIa
3. Breast cancer image, computer aided detection/diagnosis software, Class 3: Class IIb
4. Gastric cancer imaging and computer-aided detection/diagnosis software: Class IIb
5. Prostate cancer imaging and computer-aided detection/diagnosis software: Class IIb
6. Ultrasound imaging and computer-aided detection/diagnosis software, Class 3: Class IIb
IV. Conclusion
References

저자정보

  • Lee ji yoon Dipartment of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 06351, Korea
  • Ryu gyu ha Dipartment of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul 06351, Korea

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