Summary of Data-driven Analysis Of Ai in Medical Device Software in China: Deep Learning and General Ai Trends Based on Regulatory Data, by Yu Han et al.
Data-Driven Analysis of AI in Medical Device Software in China: Deep Learning and General AI Trends Based on Regulatory Data
by Yu Han, Aaron Ceross, Sarim Ather, Jeroen H.M. Bergmann
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a data-driven approach to automatically extract and analyze artificial intelligence-enabled medical devices (AIMD) from the National Medical Products Administration (NMPA) regulatory database. This study identifies over 2,000 MDSW registrations, including 531 standalone applications and 1,643 integrated within medical devices, with 43 of these being AI-enabled. The leading medical specialties utilizing AIMD include respiratory, ophthalmology/endocrinology, and orthopedics. The approach improves data extraction speed, enabling comparison and contrast across different areas. This study provides the first extensive exploration of AIMD in China, demonstrating the potential of automated regulatory data analysis in understanding and advancing AI in medical technology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) is helping make medical devices better. Researchers looked at a big database to find out how many AI-powered medical devices are being used in different fields like respiratory medicine and orthopedics. They found over 2,000 types of devices, including some that work on their own and others that are part of bigger machines. The study shows how AI can help make decisions faster and better. This is important because it helps us understand how AI is changing medical technology. |
