Data-Driven Analysis of AI in Medical Device Software in China: Trends of Deep Learning and Traditional AI Based on Regulatory Data

arXiv cs.AI / 4/27/2026

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Key Points

The study applies a data-driven, automated pipeline to extract and analyze AI-enabled medical device software from China’s NMPA regulatory database.
By screening over 4 million regulatory entries, the authors identify 2,174 MDSW registrations, including 43 AI-enabled offerings (531 standalone and 1,643 integrated across devices).
The analysis shows that respiratory (20.5%), ophthalmology/endocrinology (12.8%), and orthopedics (10.3%) are the leading specialties using AI-enabled MDSW.
The authors argue that automating regulatory-data screening supports scalable, reproducible, and quickly updatable insights for tracking trends in AI-enabled medical technology.
The work is presented as the first large-scale, data-driven exploration of AIMD in China, demonstrating the value of automated regulatory analytics for medical tech research and oversight.

Abstract

Artificial intelligence (AI) in medical device software (MDSW) represents a transformative clinical technology, attracting increasing attention within both the medical community and the regulators. In this study, we leverage a data-driven approach to automatically extract and analyze AI-enabled medical devices (AIMD) from the National Medical Products Administration (NMPA) regulatory database. The continued increase in publicly available regulatory data requires scalable methods for analysis. Automation of regulatory information screening is essential to create reproducible insights that can be quickly updated in an ever changing medical device landscape. More than 4 million entries were assessed, identifying 2,174 MDSW registrations, including 531 standalone applications and 1,643 integrated within medical devices, of which 43 were AI-enabled. It was shown that the leading medical specialties utilizing AIMD include respiratory (20.5%), ophthalmology/endocrinology (12.8%), and orthopedics (10.3%). This approach greatly improves the speed of data extracting providing a greater ability to compare and contrast. This study provides the first extensive, data-driven exploration of AIMD in China, showcasing the potential of automated regulatory data analysis in understanding and advancing the landscape of AI in medical technology.