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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Radiology and Diagnostic Technologies
  • Health Informatics and AI Applications

Background:

  • Artificial intelligence (AI) is transforming fields reliant on human cognition, with neural networks advancing data processing.
  • Convolutional neural networks (CNNs) and GPU technology have significantly improved image recognition accuracy and computational efficiency.
  • AI applications in radiology offer enhanced diagnostic accuracy and efficiency for pattern detection and classification.

Purpose of the Study:

  • To review advancements in AI-based imaging applications for diagnostic support.
  • To focus on the role of CNNs, regulatory challenges, and labor impacts in diagnostic imaging.
  • To analyze AI product development trends and key application areas in medical imaging.

Main Methods:

  • Analysis of AI product development trends from 2017 to 2020.
  • Identification of primary focus areas for AI products in medical imaging (neuroimaging, chest imaging, CT, MRI).
  • Review of AI tool applications in high-prevalence diseases (lung cancer, stroke, breast cancer).
  • Examination of regulatory compliance (MDD, MDR Class IIa/I) for AI medical devices.

Main Results:

  • Neuroimaging, chest imaging, CT, and MRI are primary areas for AI products.
  • AI tools target high-prevalence diseases like lung cancer, stroke, and breast cancer.
  • AI product development surged from 2017-2020, peaking in 2020, followed by stabilization.
  • Most AI products comply with moderate-risk standards under MDD and MDR.

Conclusions:

  • AI, particularly CNNs, holds transformative potential for enhancing diagnostic imaging and support.
  • Regulatory compliance is crucial for AI product development in radiology.
  • The integration of AI in diagnostic imaging presents both opportunities and potential challenges to human labor.