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

• URL: http://arxiv.org/abs/2411.07378v1
• Date: Mon, 11 Nov 2024 21:28:50 GMT
• Title: Data-Driven Analysis of AI in Medical Device Software in China: Deep Learning and General AI Trends Based on Regulatory Data
• Authors: Yu Han, Aaron Ceross, Sarim Ather, Jeroen H. M. Bergmann,
• Abstract summary: We leverage a data-driven approach to automatically extract and analyze AI-enabled medical devices. More than 4 million entries were assessed, identifying 2,174 MDSW registrations. Leading medical specialties utilizing AIMD include respiratory (20.5%), ophthalmology/endocrinology (12.8%), and orthopedics (10.3%)
• Score: 3.1485639585141114
• License:
• 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.

Related papers

• Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance Analysis [0.7373617024876725]
  This study fills a crucial gap in aligning XAI applications in bioelectronics with stringent provisions of EU regulations. It provides a practical framework for developers and researchers, ensuring their AI innovations adhere to legal and ethical standards.
  arXiv  Detail & Related papers  (2024-08-27T14:59:27Z)
• GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI [67.09501109871351]
  Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals. GMAI-MMBench is the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format.
  arXiv  Detail & Related papers  (2024-08-06T17:59:21Z)
• Artificial Intelligence-based Decision Support Systems for Precision and Digital Health [0.49109372384514843]
  We discuss the opportunity offered by AI, more specifically reinforcement learning, to current trends in healthcare. We focus on the area of adaptive interventions. This article has undergone anonymous review and is intended as a book chapter for the volume "Frontiers of Statistics and Data Science"
  arXiv  Detail & Related papers  (2024-07-22T21:39:34Z)
• Rapid Review of Generative AI in Smart Medical Applications [3.068678059223457]
  Generative models, a key AI technology, have revolutionized medical image generation, data analysis, and diagnosis. This article explores their application in intelligent medical devices. Generative models show great promise in medical image generation, data analysis, and diagnosis.
  arXiv  Detail & Related papers  (2024-06-08T03:34:47Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• An Explainable AI Framework for Artificial Intelligence of Medical Things [2.7774194651211217]
  We leverage a custom XAI framework, incorporating techniques such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-Cam) The proposed framework enhances the effectiveness of strategic healthcare methods and aims to instill trust and promote understanding in AI-driven medical applications. We apply the XAI framework to brain tumor detection as a use case demonstrating accurate and transparent diagnosis.
  arXiv  Detail & Related papers  (2024-03-07T01:08:41Z)
• Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey [53.691704671844406]
  The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare. The human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body. HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed. Recently, generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data.
  arXiv  Detail & Related papers  (2024-01-22T03:17:41Z)
• HEAR4Health: A blueprint for making computer audition a staple of modern healthcare [89.8799665638295]
  Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems. Computer audition can be seen to be lagging behind, at least in terms of commercial interest. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data.
  arXiv  Detail & Related papers  (2023-01-25T09:25:08Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform to Facilitate Healthcare AI Research [0.0]
  We present a system to accelerate data acquisition, dataset development and analysis, and AI model development. This system can ingest 15,000 patient records per hour, where each record represents thousands of measurements, text notes, and high resolution data.
  arXiv  Detail & Related papers  (2021-12-13T18:39:10Z)
• MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation [110.31526448744096]
  We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data. We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
  arXiv  Detail & Related papers  (2021-09-29T18:09:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.