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The new era of artificial intelligence in neuroradiology: current research and promising tools. | LitMetric

AI Article Synopsis

  • Radiology is well-suited for AI adoption because it has a digital workflow, standardized image protocols, and clear interpretive tasks, leading to over 200 FDA-approved AI products for image analysis.
  • To effectively use AI in clinical practice, radiologists need to familiarize themselves with AI applications specific to their field, especially in neuroradiology, addressing conditions like vascular diseases and neurodegenerative disorders.
  • AI algorithms have the potential to significantly enhance radiology by improving image analysis, adding value to quantitative imaging, and reducing diagnostic errors, though challenges in AI model generalization remain.

Article Abstract

Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10987255PMC
http://dx.doi.org/10.1055/s-0044-1779486DOI Listing

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