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Challenges and Potential of Artificial Intelligence in Neuroradiology.

Anthony J WinderEmma A M StanleyFiehler JensNils Daniel Forkert
Published in: Clinical neuroradiology (2024)
Translating AI methods from the research to the clinical domain is complicated by challenges related to model design and validation studies, medical product regulations, and healthcare providers' reservations regarding AI's efficacy and affordability. However, each of these limitations is also an opportunity for high-impact research that will help to accelerate the clinical adoption of state-of-the-art medical AI. In all cases, establishing and adhering to appropriate reporting standards is an important responsibility that is shared by all of the parties involved in the life cycle of a prospective AI software product.
Keyphrases
  • artificial intelligence
  • healthcare
  • machine learning
  • big data
  • deep learning
  • life cycle
  • risk assessment
  • electronic health record
  • human health
  • drug induced
  • health insurance
  • case control