[{"innerBlocks":[],"name":"core\/freeform","postId":877,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"There are four key priorities that are necessary for artificial intelligence to play a consistent role in medical imaging within clinical practice.

Outlining those challenges, opportunities and priorities for research in AI are the goals of a report published in the Journal of the American College of Radiolog<\/i>y.

The report suggests the key challenges are:

\u00b7 Creating structured AI use cases, defining and highlighting clinical challenges potentially solvable by AI.

\u00b7 Establishing methods to encourage data sharing for training and testing AI algorithms to promote generalizability to widespread clinical practice and mitigate unintended bias.

\u00b7 Establishing tools for validation and performance monitoring for AI algorithms to facilitate regulatory approval.

\u00b7 Developing standards and common data elements for seamless integration of AI tools into existing clinical workflows.

The article is a deliverable from a multi-stakeholder effort that grew out of a workshop last August, convened by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) to explore the future of AI in medical imaging.

The National Institutes of Health, the Radiological Society of North America and The Academy for Radiology and Biomedical Imaging Research co-sponsored the August workshop.

\"Although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower, because we must ensure AI in medical imaging is useful, safe, effective and easily integrated into existing radiology workflows before they can be used in routine patient care,\" says Bibb Allen, MD, workshop co-chair and chief medical officer of the ACR Data Science Institute.

\"The workshop highlighted structured AI use case development, access to diverse sources of data for training AI models, multi-site algorithm validation and monitoring the performance of these models using real-world data from clinical use as ways to accelerate the widespread deployment and clinical use of AI algorithms to improve the care we provide our patients,\" Allen recalls.

\"Radiology has transformed the practice of medicine in the past century, and AI has the potential to radically impact radiology in positive ways,\" adds Krishna Kandarpa, MD, co-author of the report and director of research sciences and strategic directions at NIBIB. \"This roadmap is a timely survey and analysis by experts at federal agencies and among our industry and professional societies that will help us take the best advantage of AI technologies as they impact the medical imaging field.\"

The article can be found here.<\/a>
","saveContent":"There are four key priorities that are necessary for artificial intelligence to play a consistent role in medical imaging within clinical practice.

Outlining those challenges, opportunities and priorities for research in AI are the goals of a report published in the Journal of the American College of Radiolog<\/i>y.

The report suggests the key challenges are:

\u00b7 Creating structured AI use cases, defining and highlighting clinical challenges potentially solvable by AI.

\u00b7 Establishing methods to encourage data sharing for training and testing AI algorithms to promote generalizability to widespread clinical practice and mitigate unintended bias.

\u00b7 Establishing tools for validation and performance monitoring for AI algorithms to facilitate regulatory approval.

\u00b7 Developing standards and common data elements for seamless integration of AI tools into existing clinical workflows.

The article is a deliverable from a multi-stakeholder effort that grew out of a workshop last August, convened by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) to explore the future of AI in medical imaging.

The National Institutes of Health, the Radiological Society of North America and The Academy for Radiology and Biomedical Imaging Research co-sponsored the August workshop.

\"Although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower, because we must ensure AI in medical imaging is useful, safe, effective and easily integrated into existing radiology workflows before they can be used in routine patient care,\" says Bibb Allen, MD, workshop co-chair and chief medical officer of the ACR Data Science Institute.

\"The workshop highlighted structured AI use case development, access to diverse sources of data for training AI models, multi-site algorithm validation and monitoring the performance of these models using real-world data from clinical use as ways to accelerate the widespread deployment and clinical use of AI algorithms to improve the care we provide our patients,\" Allen recalls.

\"Radiology has transformed the practice of medicine in the past century, and AI has the potential to radically impact radiology in positive ways,\" adds Krishna Kandarpa, MD, co-author of the report and director of research sciences and strategic directions at NIBIB. \"This roadmap is a timely survey and analysis by experts at federal agencies and among our industry and professional societies that will help us take the best advantage of AI technologies as they impact the medical imaging field.\"

The article can be found here.<\/a>
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Workgroup outlines 4 key challenges to using AI in imaging

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